OER & KI: Literatur

In diesem Bereich finden Sie eine große Bandbreite an Materialien zum Nach- und Weiterlesen. Hierzu gehören neben aktueller (Forschungs-)Literatur auch Broschüren, Blogartikel und weitere Veröffentlichungen zum Thema.

Chancen und Potenziale

Dieses Themenfeld umfasst die Frage, welche Möglichkeiten KI für OER und Open Education eröffnet. Die Literatur diskutiert insbesondere die Unterstützung bei der Erstellung und Weiterentwicklung von OER, die Verbesserung von Auffindbarkeit und Organisation offener Bildungsressourcen sowie die Personalisierung von Lernprozessen. Besonders stark vertreten sind Beiträge zu Empfehlungssystemen, adaptiven Lernpfaden, automatischer Verschlagwortung und KI-gestützter Materialentwicklung.

Bozkurt (2023): Generative AI, Synthetic Contents, Open Educational Resources (OER), and Open Educational Practices (OEP): A New Front in the Openness Landscape

  • Es geht um Generative KI als neue Entwicklungslinie im Open-Education-Diskurs; verbindet OER, OEP und synthetische Inhalte.

Tlili & Burgos (2024): Unleashing the Power of Open Educational Practices (OEP) through Artificial Intelligence (AI): Where to Begin

  • Hier liegt ein Fokus auf KI als Unterstützung für Open Educational Practices, u. a. Personalisierung, Kollaboration und Zugänglichkeit.

UNESCO (2024): Dubai Declaration on Open Educational Resources

  • Die strategische UNESCO-Perspektive auf OER, KI, Digital Public Goods und offene Bildungsökosysteme.

Wannemacher et al. (2025)

  • Wichtige deutschsprachige Überblicksquelle zu Potenzialen, Herausforderungen und Perspektiven der KI-Nutzung für Open Education.

Panke (2024): Open Educational Resources and Artificial Intelligence for Future Open Education

  • Zukunftsperspektive auf OER und KI; besonders relevant für offene Bildung, OEP und kollaborative Wissensproduktion.

Tlili & Burgos (2024): Unleashing the Power of Open Educational Practices Through AI

  • Internationale Perspektive. Es geht um Potenziale von KI für OER und OEP.

Fischer et al. (2024): OER und KI – Entwicklungschancen für die berufliche Weiterbildung

  • Deutschsprachiger Überblick. Es geht um Chancen von KI für OER mit Schwerpunkt auf den Weiterbildungskontext.

OER Africa (2023): Three Ways AI Could Change How We Use OER

  • Bietet einen niedrigschwelligen Überblick.

Wiley (2024): How Generative AI Affects OER

  • Hier wird der Übergang zu „AI-drafted OER“ diskutiert.

Mormando (2023): AI’s Role in Revolutionizing OER

  • Hier findet man einen Überblick über Einsatzmöglichkeiten von KI im OER-Kontext, mit Fokus auf Materialentwicklung.

Papazian & Pardos (2025). Frankenstein Curricula: Stitching Together Open Educational Resources to Generate Dynamic Learning Paths With AI

  • Es geht um adaptive Lernpfade, KI-generierte Curricula, Personalisierung von OER-Nutzung

Tavakoli et al. (2022): An AI-based Open Recommender System for Personalized Labor Market Driven Learning

  • Es geht um Empfehlungssysteme, Personalisierung, OER-Recommendation

Li et al. (2024): Aligning Open Educational Resources to New Taxonomies: How AI Technologies Can Help

  • Es geht um automatische Klassifikation, Metadaten, Taxonomien, OER-Infrastruktur

Chounta (2019): A Review of the State-of-the-Art of the Use of Machine Learning and AI by Educational Portals and OER Repositories

  • Es geht um Student Modeling, Recommendation Systems, Learning Analytics, OER-Repositorien

Shilibekova (2025): AI-Enhanced OER in a Non-English Context

  • Fokus auf Übersetzung, Lokalisierung und nicht-englischsprachige Bildungskontexte

Herausforderungen und Risiken

Dieses Themenfeld beschäftigt sich mit den Herausforderungen, Unsicherheiten und potenziellen negativen Folgen des Einsatzes von KI im Kontext von OER und Open Education. Diskutiert werden insbesondere Qualitätsprobleme, Halluzinationen, Bias, Datenschutz, Prüfungs- und Bewertungsfragen, Nachhaltigkeit sowie Fragen von Bildungsgerechtigkeit und gesellschaftlicher Verantwortung.

Clark et al. (2025): Auto-Evaluation: A Critical Measure in Driving Improvements in Quality and Safety of AI-Generated Lesson Resources

  • Es geht um Qualitätssicherung, Evaluation und Verlässlichkeit KI-generierter Lernmaterialien.

Qi & Wang (2024): Learning Assessment for Open Education Learners in the Era of Generative Artificial Intelligence

  • Thematisieren Folgen generativer KI für Assessment, Prüfungen und Leistungsbewertung.

Farrow (2023): The Possibilities and Limits of Explainable Artificial Intelligence (XAI) in Education: A Socio-Technical Perspective

  • Hier werden Transparenz, Nachvollziehbarkeit, Explainable AI und Vertrauenswürdigkeit thematisiert.

Pietrusky (2025): KI und offene Bildung – Bildung ohne künstliche Barrieren. Mit KI-Kompetenz und Open Source für mehr Gerechtigkeit bei der individuellen Kompetenzentwicklung

  • Bildungsgerechtigkeit, Open Source, KI-Kompetenzen und soziale Folgen proprietärer KI-Systeme stehen im Fokus.

Aksoy & Kursun (2024): Behind the Scenes: A Critical Perspective on GenAI and Open Educational Practices

  • Hir wird eine kritische Perspektive auf Risiken, Verzerrungen und problematische Entwicklungen im Zusammenspiel von KI und Open Education aufgemacht.

Tlili et al. (2026): The Double-Edged Sword: Open Educational Resources in the Era of Generative Artificial Intelligence

  • Hier geht es um OER als „zweischneidiges Schwert“ im KI-Zeitalter.

Tlili et al. (2024): Taming the Monster: How Can Open Education Promote the Effective and Safe Use of Generative AI in Education

  • Sicherer und verantwortungsvoller Einsatz generativer KI werden hier thematisiert.

Mills et al. (2023): How Do We Respond to Generative AI in Education? Open Educational Practices Give Us a Framework for an Ongoing Process

  • Hier geht es unter anderem um KI als Herausforderung für bestehende Bildungspraktiken und Rollenbilder.

Schlodtfeld (2025): 10 Fragen zu KI-Output, Urheberrecht und OER

  • Hierbei handelt es sich um eine praxisorientierte Auseinandersetzung mit Risiken und Unsicherheiten bei KI-generierten Inhalten.

Shilibekova (2025): Addressing Challenges in Faculty Professional Development: UDL Training through AI-Enhanced OER in a Non-English Context

  • Herausforderungen der Lokalisierung und kulturellen Anpassung stehen hier im Fokus.

Evenstein Sigalov et al. (2025): Transforming Higher Education: A Decade of Integrating Wikipedia and Wikidata for Literacy Enhancement and Social Impact

  • Es geht um Wissenslücken, Diversität, Bias und Repräsentationsfragen in offenen Wissensräumen.

Gunder et al. (2025): AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

  • Es geht um Ethische Fragen, AI Literacies, globale Perspektiven auf Chancen und Risiken.

Recht, Offenheit & Governance

Dieses Themenfeld beschäftigt sich mit den rechtlichen, politischen und normativen Rahmenbedingungen des Zusammenspiels von OER und Künstlicher Intelligenz. Im Mittelpunkt stehen Fragen des Urheberrechts, der Lizenzierung KI-generierter Inhalte, der Nutzung von Trainingsdaten sowie der Transparenz und Offenheit von KI-Systemen. Darüber hinaus wird diskutiert, welche Rolle offene Infrastrukturen, Open-Source-KI, Digital Public Goods und regulatorische Rahmenbedingungen für die zukünftige Entwicklung offener Bildungsökosysteme spielen. Das Themenfeld erweitert damit die klassische OER-Diskussion von offenen Materialien hin zu Fragen der Governance, Kontrolle und Gestaltung von KI-gestützten Bildungs- und Wissensinfrastrukturen.

Rack (2024): Generative KI und Urheberrecht – Überblick über zentrale Rechtsfragen

  • Zentrale deutschsprachige Quelle zu urheberrechtlichen Fragen generativer KI. Behandelt insbesondere Training mit urheberrechtlich geschützten Werken, Text-and-Data-Mining sowie rechtliche Rahmenbedingungen für KI und OER.

Rack (2023): OER und generative KI – Creative Commons und KI-generierte Inhalte

  • Fokus auf die Lizenzierung von KI-generierten Inhalten im OER-Kontext. Diskutiert Public Domain Mark, CC-Lizenzen und die Frage, wem KI-generierter Output gehört.

Schlodtfeld (2025): 10 Fragen zu KI-Output, Urheberrecht und OER

  • Praxisorientierte Handreichung der HOOU. Behandelt Fragen zu KI-Output, Kennzeichnung, Urheberrecht, Remix, Creative-Commons-Lizenzen und OER.

UNESCO (2024): Dubai Declaration on Open Educational Resources

  • Die strategische UNESCO-Perspektive auf OER, KI, Digital Public Goods und offene Bildungsökosysteme. Betont die Bedeutung offener, gemeinwohlorientierter Infrastrukturen.

Wikimedia (2024): Offene KI für Alle!

  • Plädiert für offene und gemeinwohlorientierte KI-Infrastrukturen. Diskutiert Offenheit, Transparenz und demokratische Kontrolle von KI-Systemen.

Pietrusky (2025): KI und offene Bildung – Bildung ohne künstliche Barrieren

  • Verbindet Open-Source-KI mit Bildungsgerechtigkeit. Zeigt auf, wie proprietäre KI-Systeme Bildungsungleichheiten verstärken können und welche Rolle offene KI-Modelle spielen könnten.

Bozkurt (2023): Generative AI, Synthetic Contents, Open Educational Resources (OER), and Open Educational Practices (OEP): A New Front in the Openness Landscape

  • Diskutiert generative KI als neue Entwicklungslinie der Open-Education-Bewegung. Thematisiert dabei auch Spannungen zwischen Offenheit, Kommerzialisierung und KI.

Tlili et al. (2026): The Double-Edged Sword: Open Educational Resources in the Era of Generative Artificial Intelligence

  • Kritische Auseinandersetzung mit Chancen und Risiken generativer KI für OER. Behandelt auch Fragen von Offenheit, Kontrolle und Governance.

Duan (2026): The Evolution of Open Educational Practices: A Comparative Analysis of Models and a New Framework for the AI Era

  • Beschreibt die Entwicklung von OER und OEP hin zu KI-gestützten Bildungsökosystemen. Besonders relevant für Governance-Fragen, da KI hier zunehmend als Infrastruktur und nicht nur als Werkzeug verstanden wird.

Wannemacher et al. (2025): Perspektiven der KI-Nutzung für die Open Education

  • Deutschsprachige Überblicksquelle zu Potenzialen, Herausforderungen und Entwicklungsperspektiven von KI im Open-Education-Kontext. Bezieht auch strategische und governancebezogene Fragestellungen ein.

Weimer et al. (2026): Open Educational Resources (OER) in Deutschland – Teil eines Open Science Kulturwandels

  • Verortet KI und OER im Kontext eines umfassenderen Open-Science-Kulturwandels und diskutiert strukturelle Rahmenbedingungen offener Bildung.

Fischer et al. (2024): OER und KI – Entwicklungschancen für die berufliche Weiterbildung

  • Überblick über Chancen und Herausforderungen von KI im OER-Kontext. Enthält auch Bezüge zu regulatorischen und organisatorischen Rahmenbedingungen.

Gunder et al. (2025): AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

  • Diskutiert AI Literacies, offene Bildungskulturen und die Frage, wie Offenheit in einer zunehmend KI-geprägten Bildungswelt erhalten und weiterentwickelt werden kann.

Panke (2024): Open Educational Resources and Artificial Intelligence for Future Open Education

  • Zukunftsperspektive auf OER und KI. Thematisiert neben Potenzialen auch strukturelle und gesellschaftliche Rahmenbedingungen zukünftiger Open-Education-Ökosysteme.

Community, OEP & Kulturwandel

Dieses Themenfeld beschäftigt sich mit der Frage, wie Künstliche Intelligenz offene Bildungskulturen, Bildungspraktiken und Formen gemeinsamer Wissensproduktion verändert. Im Mittelpunkt stehen Open Educational Practices (OEP), Open Pedagogy, kollaborative Wissensproduktion, Communities und neue Rollen von Lehrenden und Lernenden. Die Literatur diskutiert dabei zunehmend, wie KI nicht nur die Erstellung von Ressourcen beeinflusst, sondern auch Lernprozesse, Partizipation, Kooperation und die Gestaltung offener Bildungsökosysteme verändert.

Duan (2026): The Evolution of Open Educational Practices: A Comparative Analysis of Models and a New Framework for the AI Era

  • Systematische Analyse von 14 OEP-Modellen. Beschreibt die Entwicklung von OER über Open Pedagogy hin zu KI-gestützten Bildungsökosystemen und einem „AI-Empowered Open TPACK Framework“.

Gunder et al. (2025): AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

  • Verbindet AI Literacies mit Open Educational Practices und dem Konzept einer „Opened Culture“. Thematisiert Zusammenarbeit, Gemeinschaft, Offenheit und die Rolle von KI in offenen Bildungskulturen.

Mills et al. (2023): How Do We Respond to Generative AI in Education? Open Educational Practices Give Us a Framework for an Ongoing Process

  • Argumentiert, dass Open Educational Practices einen geeigneten Rahmen bieten, um auf die Herausforderungen generativer KI zu reagieren. Stellt OEP als Alternative zu rein technischen oder regulatorischen Antworten dar.

Evenstein Sigalov et al. (2025): Transforming Higher Education: A Decade of Integrating Wikipedia and Wikidata for Literacy Enhancement and Social Impact

Langzeitstudie zur Nutzung von Wikipedia und Wikidata als offene Lernplattformen. Fokus auf kollaborative Wissensproduktion, gesellschaftliche Wirkung und OER-Erstellung durch Studierende.

Tlili & Burgos (2024): Unleashing the Power of Open Educational Practices (OEP) through Artificial Intelligence (AI): Where to Begin

  • Internationale Perspektive auf die Verbindung von KI und OEP. Thematisiert Partizipation, Kollaboration und neue Formen offener Bildungspraktiken.

Tawileh & Al-Smadi (2025): From Open Educational Resources to Open Pedagogy – Navigating the AI-Powered Future of Digital Learning

  • Beschreibt die Entwicklung von OER hin zu Open Pedagogy und diskutiert die Rolle von KI innerhalb offener Lehr- und Lernprozesse.

Bozkurt (2023): Generative AI, Synthetic Contents, Open Educational Resources (OER), and Open Educational Practices (OEP): A New Front in the Openness Landscape

  • Diskutiert generative KI als neue Entwicklungslinie innerhalb der Open-Education-Bewegung und thematisiert deren Auswirkungen auf OEP und offene Wissenskulturen.

Panke (2024): Open Educational Resources and Artificial Intelligence for Future Open Education

  • Zukunftsperspektive auf OER und KI. Besonders relevant für Open Educational Practices, offene Lehrbuchprojekte und kollaborative Wissensproduktion.

Bucio-García (2025): Creating Open Learning Virtuous Circles: Wikipedia, Generative AI, and Postdigital Literacies in a Social Service Program for Online Students

  • Verbindet Wikipedia, Generative KI und offene Lernpraktiken. Fokus auf Co-Creation, digitale Literacies und gemeinschaftliche Wissensproduktion.

Bättig Neusch et al. (2025): Das Kollektiv hinter dem Individuum – OER, KI

  • Diskutiert die Bedeutung kollektiver Wissensproduktion und gemeinschaftlicher Prozesse im Zusammenspiel von OER und KI.

Infrastruktur & Orientierung

Dieses Themenfeld beschäftigt sich mit den infrastrukturellen Voraussetzungen offener Bildung im KI-Zeitalter. Im Mittelpunkt stehen OER-Repositorien, Wissensplattformen, Metadaten, Standards, Interoperabilität, Nachhaltigkeit sowie die Frage, wie Orientierung in einem zunehmend komplexen Feld geschaffen werden kann. Die Literatur diskutiert dabei nicht nur technische Infrastrukturen, sondern auch Wissensorganisation, Kompetenzaufbau und die Gestaltung offener Bildungsökosysteme, die den Umgang mit KI und OER langfristig unterstützen.

Wannemacher et al. (2025): Perspektiven der KI-Nutzung für die Open Education

  • Wichtige deutschsprachige Überblicksquelle. Thematisiert Potenziale, Herausforderungen und strukturelle Voraussetzungen der KI-Nutzung in Open Education.

Wannemacher et al. (2026): KI-Nutzung und OER

  • Fokus auf Orientierungsbedarfe, Kompetenzentwicklung und die strukturellen Voraussetzungen für die Nutzung von KI im OER-Kontext.

Chounta (2019): A Review of the State-of-the-Art of the Use of Machine Learning and AI by Educational Portals and OER Repositories

  • Frühe Überblicksarbeit zu KI in OER-Repositorien und Bildungsportalen. Thematisiert Empfehlungssysteme, Learning Analytics, OER-Repositorien und infrastrukturelle Herausforderungen.

Li et al. (2024): Aligning Open Educational Resources to New Taxonomies: How AI Technologies Can Help

  • Beschäftigt sich mit Taxonomien, Metadaten und automatischer Klassifikation von OER. Besonders relevant für Interoperabilität und Wissensorganisation.

Miranda et al. (2025): Design for Sustainability of Open Education Resources in the Era of AI: A Case Study

  • Fokus auf Nachhaltigkeit offener Bildungsressourcen im KI-Zeitalter. Verbindet technologische, pädagogische und organisatorische Perspektiven.

UNESCO (2024): Dubai Declaration on Open Educational Resources

  • Betont die Bedeutung offener Wissensinfrastrukturen, Digital Public Goods und nachhaltiger Bildungsökosysteme.

ENCORE+ (2023): From OER to OAI – What’s Next?

  • Diskutiert Interoperabilität, offene Infrastrukturen und zukünftige Entwicklungen offener Bildungsökosysteme.

Gunder et al. (2025): AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices

  • Verbindet AI Literacies mit Orientierungskompetenz und der Fähigkeit, sich in komplexen offenen Bildungsökosystemen zu bewegen.

Arantes do Amaral (2025): Enhancing MOOCs with AI. The AI + Open Education Initiative

  • Thematisiert KI-gestützte offene Lernumgebungen und die Rolle von Plattformen als Bildungsinfrastrukturen.

Ahmadfard (2026): Advancing Open Learning through Interactive Open Educational Resources

  • Fokus auf interaktive OER und zukünftige Entwicklungsrichtungen offener Lerninfrastrukturen.

Pietrusky (2025): KI und offene Bildung – Bildung ohne künstliche Barrieren

  • Diskutiert Open-Source-KI, lokale Infrastrukturen und die Bedeutung offener Technologien für Bildungsgerechtigkeit und langfristige Zugänglichkeit.

Gesamtübersicht

Tavakoli, M., Faraji, A., Vrolijk, J., Molavi, M., Mol, S. T., & Kismihók, G. (2022). An AI-based open recommender system for personalized labor market driven education. Advanced Engineering Informatics, 52, 101508. Zum Artikel

Inhalte:
Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements; (2) decomposes skills into learning topics; (3) collects a variety of open online educational resources that address those topics; (4) checks the quality of those resources and topic relevance with three intelligent prediction models; (5) helps learners to set their learning goals towards their desired job-related skills; (6) recommends personalized learning pathways and learning content based on individual learning goals; and (7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by means of a pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal recommendations provided by eDoer to acquire knowledge of basic statistics, attained higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported.

Bozkurt, A. (2023). Generative AI, synthetic contents, open educational resources (OER), and open educational practices (OEP): A new front in the openness landscape. Open Praxis, 15(3), 178–184. Zum Artikel

Inhalte:
This paper critically examines the transformation of the educational landscape through the integration of generative AI with Open Educational Resources (OER) and Open Educational Practices (OEP). The emergence of AI in content creation has ignited debate regarding its potential to comprehend and generate human language, creating content that is often indistinguishable from that produced by humans. This shift from organic (human-created) to synthetic (AI-created) content presents a new frontier in the educational sphere, particularly in the context of OER and OEP. The paper explores the generative AI’s capabilities in OER and OEP, such as automatic content generation, resource curation, updating existing resources, co-creation and facilitating collaborative learning. Nevertheless, it underscores the importance of addressing challenges like the quality and reliability of AI-generated content, data privacy, and equitable access to AI technologies. The critical discussion extends to a contentious issue, ownership in OER/OEP. While AI-generated works lack human authorship and copyright protection, the question of legal liability and recognition of authorship remains a significant concern. In response, the concept of prompt engineering and co-creation with AI is presented as a potential solution, viewing AI not as authors, but powerful tools augmenting authors’ abilities. By examining generative AI’s integration with OER and OEP, this paper encourages further research and discussion to harness AI’s power while addressing potential concerns, thereby contributing to the dialogue on responsible and effective use of generative AI in education.

ENCORE+ (2023). From OER to OAI: what’s next now we all play with ChatGPT? Netzwerktreffen am .
Positionspapier. Zum Positionspapier.

Inhalte:
This fourth technological position paper has aimed to investigate the impact of LLMs (Large Language Models) like ChatGPT on the OER ecosystem. The ENCORE+ Networks, including stakeholders with different backgrounds, including academia and companies, has discussed opportunities and challenges posed by the adoption of intelligent agents. The case of ChatGPT allowed a discussion about the role of Technology in the OER Ecosystem, connected to conceiving of OER mostly as data than books, and the implications at the policy level (copyright is challenged) and economic level, due to new business models provided by the ed-tech landscape. H5P, an open-source content collaboration framework1 presents a good practice related to the reuse and transformation of existing static content, extracting the main concepts and enriching them with a quiz, glossary, and concept cards.

Farrow, R. (2023). The possibilities and limits of XAI in education: A socio‑technical perspective. Learning, Media and Technology, 48(2), 266–279. Zum Artikel

Inhalte:
Explicable AI in education (XAIED) has been proposed as a way to improve trust and ethical practice in algorithmic education. Based on a critical review of the literature, this paper argues that XAI should be understood as part of a wider socio-technical turn in AI. The socio-technical perspective indicates that explicability is a relative term. Consequently, XAIED mediation strategies developed and implemented across education stakeholder communities using language that is not just ‘explicable’ from an expert or technical standpoint, but explainable and interpretable to a range of stakeholders including learners. The discussion considers the impact of XAIED on several educational stakeholder types in light of the transparency of algorithms and the approach taken to explaination. Problematising the propositions of XAIED shows that XAI is not a full solution to the issues raised by AI, but a beginning and necessary precondition for meaningful discourse about possible futures.

Göçmez, L., Okur, M. R. (2023). Artificial Intelligence Applications in Open and Distance Education: A Systematic Review of the Articles (2007–2021). Zum Artikel

Inhalte:
With the urgent shift to distance learning due to Covid-19 measures, educational institutions around the world have started to adopt e-learning massively. According to many experts, artificial intelligence (AI) may provide both system-wide and pedagogical solutions to the problems which the administrators, educators, and students encounter during e-learning. The purpose of this study was to systematically review articles on AI applications in open and distance education between the years 2007 and 2021 in order to visualize the potential of intelligent educational systems. Thus, 171 selected articles regarding explicit inclusion and exclusion criteria, were analysed in terms of the year/ place of the paper, affiliation of the first author, the method applied, and the journals they published in. Then the research areas of AI applications in distance education were coded iteratively. The results indicated that most of the articles were published after 2018 mainly by Chinese, Spanish, Turkish, American, and Indian authors in computer science and STEM disciplines. Moreover, there is a sharp increase in publishing on this topic, especially during Covid-19 pandemics. Consequently, the analysis resulted in six areas of AI applications in open and distance education: 1. ITSs, 2. Adaptive systems and personalisation, 3. assessment and evaluation, 4. learner analytics, 5. affect recognition, and 6. Virtual learning environments, with 28 subcategories. In summary, our review results that intelligent systems can be effectively used for both system-wide solutions and pedagogical solutions to the problems such as the low level of adaptivity, collaboration, academic achievement, or engagement, and the high level of drop-out that we usually experience in distance learning via the use of adaptive expert systems, intelligent tutors or agents, chatbots, data analytics, and recommender systems.

OER Afrika (2023). Three ways Artificial Intelligence could change how we use Open Educational Resources.
Zum Artikel.

Rack, F. (2023): OER und CC-Lieznzen bei generativer KI. Zum Artikel

Inhalte:
Spätestens seit das Thema KI in aller Munde ist, wird in diesem Zusammenhang eine Frage immer wieder gestellt: Wie stehen künstliche Intelligenz und das Urheberrecht zueinander? Wann beim Einsatz von generativer KI ein Urheberrecht entsteht, haben wir hier dargestellt. Kurz zusammengefasst: Wer ein KI-System per Prompt unspezifisch ansteuert, sich also etwa einen Text oder ein Bild im Stil von … generieren lässt und diesen Output nicht weiter verändert, gewinnt daran kein Urheberrecht. Solch ein KI-Output ist gemeinfrei. Gemeinfreier KI-Output ist von sich aus open: Das Urheberrecht steht dem Weitergeben, Kopieren und Remixen von gemeinfreien Inhalten nicht im Weg.
Allerdings ist der fehlende Urheberrechtsschutz den KI-generierten Materialien von außen nicht anzusehen. Außerdem gibt es bestimmte Fälle, in denen im KI-Kontext dann doch ein Urheberrechtsschutz entsteht (siehe Fall 3). Das heißt: Schöpfer*innen und Nutzer*innen müssen sich also auch bei generativer KI und OER Gedanken um das Urheberrecht und freie Lizenzen machen.

Rack, F. (2023). Künstliche Intelligenz und Open Educational Resources. Beitrag auf iRights.info. Zum Artikel

Inhalte:
KI-Technologien laden dazu ein, wie durch Zauberhand neue Texte, Bilder oder Musik generieren zu lassen. Unter bestimmten Umständen eignen sich KI-Schöpfungen gut als OER. Doch die Entwicklung ist dynamisch. Nicht auf alle urheberrechtlichen Fragen gibt es derzeit Antworten.

Rack, F. (2023). OER, generative KI und fremde Werke. iRights.info. Zum Artikel

Inhalte:
Setzt man Technologien Künstlicher Intelligenz für Bildung und Lehre ein, ergeben sich verschiedene (urheber-)rechtliche Fragen. Fabian Rack erklärt, wie offene Bildungsmaterialien für das Training von KI-Generatoren dienen und was es beim Prompten mit Fremdwerken zu beachten gilt.

Mills, A., Bali, M., & Eaton, L. (2023). How do we respond to generative AI in education? Open educational practices give us a framework for an ongoing process. Journal of Applied Learning & Teaching, 6(1), 16–30. Zum Artikel

Inhalte:
2022, the field of higher education rapidly became aware that generative AI can complete or assist in many of the kinds of tasks traditionally used for assessment. This has come as a shock, on the heels of the shock of the pandemic. How should assessment practices change? Should we teach about generative AI or use it pedagogically? If so, how? Here, we propose that a set of open educational practices, inspired by both the Open Educational Resources (OER) movement and digital collaboration practices popularized in the pandemic, can help educators cope and perhaps thrive in an era of rapidly evolving AI. These practices include turning toward online communities that cross institutional and disciplinary boundaries. Social media, listservs, groups, and public annotation can be spaces for educators to share early, rough ideas and practices and reflect on these as we explore emergent responses to AI. These communities can facilitate crowdsourced curation of articles and learning materials. Licensing such resources for reuse and adaptation allows us to build on what others have done and update resources. Collaborating with students allows emergent, student-centered, and student-guided approaches as we learn together about AI and contribute to societal discussions about its future. We suggest approaching all these modes of response to AI as provisional and subject to reflection and revision with respect to core values and educational philosophies. In this way, we can be quicker and more agile even as the technology continues to change.
We give examples of these practices from the Spring of 2023 and call for recognition of their value and for material support for them going forward. These open practices can help us collaborate across institutions, countries, and established power dynamics to enable a richer, more justly distributed emerging response to AI.
Keywords: ChatGPT; entangled pedagogy; generative AI;

Mormando, S. (2023). AI’s role in revolutionizing open educational resources (OER). Medium. Zum Artikel

Inhalte:
The pressure on educators to provide diverse, up-to-date, and engaging learning materials is immense. This isn’t new for teachers and one of the main reasons why my district joined the #GoOpen movement many years ago. But, creating and updating OER has been time and resource-intensive. But no more, thanks to artificial intelligence(AI).
AI’s capability to generate educational content is nothing short of revolutionary. AI systems can already create textbooks, lessons, and activities tailored to specific curriculum guidelines and learning objectives. This new technology has significantly expanded the availability of OER, and has provided teachers and students with unprecedented access to customized learning materials.
Beyond content creation, AI can also play a crucial role in curating and recommending OERs. By analyzing a student’s performance data and interests, AI can suggest resources that are most likely to be beneficial. This personalized approach not only enhances the learning experience but also ensures that each student receives the most relevant and effective educational materials.

Tila, D., & Levy, D. (2023). Curating OER content through AI and ChatGPT. Open Journal of Social Sciences, 11(12), 510–527. Zum Artikel

Inhalte:
Technological advancements have always influenced academia; however, the proliferation of Artificial Intelligence (AI) is having a significantly extensive impact. Technology has already made it possible for Open Educational Resources (OER) to provide students with freely accessible course content thereby breaking the cost barriers set by commercially printed textbooks. Now, with the increasing use and availability of AI, this paper takes it a step further by investigating the potential for generative artificial intelligence tools, such as ChatGPT, to further refine the quality of OERs and improve open pedagogy processes and content. Could AI tools be a way to improve the process of curating and creating content by faculty and students? This paper shares students’ and faculty’s perceptions of AI’s impact on students’ learning and academic experiences. Faculty and students expressed divergent opinions, with students being much more optimistic about the benefits of AI than their faculty, who cited concerns about the dangers of delegating writing and critical thinking to AI. Nevertheless, both students and faculty members recognized that AI is an inevitable part of our futures that cannot be ignored. To this point, the data suggest that a viable solution involves opening conversation between faculty and students about the benefits of properly using AI tools and educating students about how to use the tools ethically. Furthermore, in terms of an OER and open pedagogy framework, by allowing students to fact-check and curate course material using AI generated and regenerated material students can learn to use AI tools to create original and thoughtful material by critically analyzing and curating the AI-sourced materials.

Alles, Susanne & Behrens, Sarah-Isabella & Brandenburger, Bonny & Franke, Katharina & Friedrichs, Linn & Hirsch, Nele & Klar, Maria & Kleinesper, Celestine & Kölling, Britta & Kranz, Uwe & Kressig, Thorsten & Kracht, Frauke & Löser, Kai & Lohrer, Jörg & Mundorf, Margret & Menne, Andreas & Riebe, Katharina & Schampijer, Simon & Schleiss, Johannes & Wissmann, Svenja. (2024). Offene KI für alle! Empfehlungen für offene und gemeinwohlorientierte KI-Technologien im Bildungsbereich. Zu den Empfehlungen

Inhalte:
Dieses Papier richtet sich an Bildungs- und Digitalpolitiker*innen aus Bund und Ländern, die sich für die Förderung offener und gemeinwohlorientierter KI in der Bildungspraxis einsetzen möchten. Es richtet sich an Personen, die KI-Anwendungen in der Bildung fördern möchten, die zum einen auf offenen Trainingsdaten und Modellen beruhen und die zum anderen vor dem Hintergrund pädagogischer und gemeinwohlorientierter Ziele entwickelt werden. Die Empfehlungen wurden in einem kollaborativen, mehrmonatigen Prozess zwischen November 2023 und Mai 2024 erarbeitet. Die Beteiligten kamen aus verschiedenen Bildungssektoren: Dozierende aus Universitäten und Hochschulen, Lehrkräfte allgemeinbildender und beruflicher Schulen, Lehrpersonen aus Erwachsenenbildungsinstituten, Mitarbeitende aus Fortbildungsinstituten der Bundesländer, aus politischen Stiftungen sowie Bildungsstiftungen und -organisationen. Motivation für die gemeinsame Arbeit war der Wunsch, die Auswahl, die Nutzung und den Einsatz von KI-Anwendungen in der Bildung nicht nur zu beobachten, sondern neue Impulse in die Entscheidungsprozesse einzubringen. Während zunehmend proprietäre und geschlossene KI-Anwendungen in den Bildungsmarkt drängen, soll mit dem Empfehlungspapier der politische und gesellschaftliche Diskurs zu zentralen Fragen wie dem Zugang zu Bildungstechnologien, zu offenen Bildungspraktiken und zu Grundrechten im digitalen Raum vorangetrieben werden. Wir empfehlen die öffentlich finanzierte Entwicklung gemeinwohlorientierter und offener KI-Technologien für den Bildungsbereich mit gleichen Zugangsmöglichkeiten für alle. Alle eingesetzten KI-Systeme sollen vor ihrem Einsatz im Bildungsbereich unabhängig geprüft werden. Um dieses Ziel zu erreichen, bedarf es diverser Maßnahmen, die kurz- bis mittelfristig umzusetzen sind.

Aksoy, D. A., & Kursun, E. (2024). Behind the scenes: A critical perspective on GenAI and open educational practices. *Open Praxis, 16*(3), 457–470. Zum Artikel

Inhalte:
Artificial Intelligence (AI) is a rapidly evolving field that is influencing every aspect of life. Generative AI (GenAI) as a sub-branch of AI is used to create content in various formats such as text, images, video, and audio. This paper discusses the implications of GenAI for Open Educational Practices (OEP), highlighting the potential affordances and challenges. GenAI can address the challenges within the OEP by leveraging openness and ethical use. GenAI’s “generative” nature and human-like language capability can provide resources such as course material, activities, examples, questions, assessment, and learning outcomes in the context of OEP. With machine learning and deep learning infrastructure, it can support the discoverability and accessibility of open resources by increasing the metadata quality. GenAI can automatically score student assignments, answer their questions, and provide instant feedback to address the lack of interaction and feedback that arises due to the large number of students, especially in massive open online courses (MOOC). On the other hand, GenAI brings challenges such as data privacy and security, copyright, biased outputs, and the generation of false information. The conclusions emphasize the importance of a nuanced approach that considers not only the advantages but also the risks associated with adopting GenAI in the OEP world. Researching and developing how to apply these technologies to education is important to shape the future of education.

Alfirević, N., Praničević, D. G., & Mabić, M. (2024). Custom-Trained Large Language Models as Open Educational Resources: An Exploratory Research of a Business Management Educational Chatbot in Croatia and Bosnia and Herzegovina. Sustainability, 16(12), 4929. Zum Artikel

Inhalte:
This paper explores the contribution of custom-trained Large Language Models (LLMs) to developing Open Education Resources (OERs) in higher education. Our empirical analysis is based on the case of a custom LLM specialized for teaching business management in higher education. This custom LLM has been conceptualized as a virtual teaching companion, aimed to serve as an OER, and trained using the authors’ licensed educational materials. It has been designed without coding or specialized machine learning tools using the commercially available ChatGPT Plus tool and a third-party Artificial Intelligence (AI) chatbot delivery service. This new breed of AI tools has the potential for wide implementation, as they can be designed by faculty using only conventional LLM prompting techniques in plain English. This paper focuses on the opportunities for custom-trained LLMs to create Open Educational Resources (OERs) and democratize academic teaching and learning. Our approach to AI chatbot evaluation is based on a mixed-mode approach, combining a qualitative analysis of expert opinions with a subsequent (quantitative) student survey. We have collected and analyzed responses from four subject experts and 204 business students at the Faculty of Economics, Business and Tourism Split (Croatia) and Faculty of Economics Mostar (Bosnia and Herzegovina). We used thematic analysis in the qualitative segment of our research. In the quantitative segment of empirical research, we used statistical methods and the SPSS 25 software package to analyze student responses to the modified BUS-15 questionnaire. Research results show that students positively evaluate the business management learning chatbot and consider it useful and responsive. However, interviewed experts raised concerns about the adequacy of chatbot answers to complex queries. They suggested that the custom-trained LLM lags behind the generic LLMs (such as ChatGPT, Gemini, and others). These findings suggest that custom LLMs might be useful tools for developing OERs in higher education. However, their training data, conversational capabilities, technical execution, and response speed must be monitored and improved. Since this research presents a novelty in the extant literature on AI in education, it requires further research on custom GPTs in education, including their use in multiple academic disciplines and contexts.

Durak, G., Çankaya, S., Özdemir, D., & Can, S. (2024). Artificial Intelligence in Education: A Bibliometric Study on Its Role in Transforming Teaching and Learning. The International Review of Research in Open and Distributed Learning, 25(3), 219–244. Zum Artikel

Inhalte

This study aimed to present a comprehensive bibliometric analysis of 1,726 academic studies from among those indexed by the Web of Science database platform between 2013 and 2023, to provide a general framework for the concept of artificial intelligence in education (AIEd). Trends in publications and citations across countries, institutions, academic journals, and authors were identified, as well as collaborations among these elements. Several bibliometric analysis techniques were applied, and for each analysis, the motivations behind the execution and method of producing findings were documented. Our findings showed that the number of studies on the concept of AIEd has increased significantly over time, with the U.S. and China being the most common countries of origin. Institutions in the U.S. stand out from those around the world. Pioneering journals in education have also emerged as prominent in the field of AIEd. On the other hand, collaboration between authors has been limited. The study was supplemented with keyword analysis to reveal thematic AIEd concepts and to reflect changing trends. For those exploring artificial intelligence in education, our insights on popular topics offer valuable guidance toward greater understanding of the latest advancements and key research areas.

Fischer, A., Jöchner, A., & Dauser, D. (2024). Open Educational Resources (OER) und Künstliche Intelligenz (KI) – Entwicklungschancen für die berufliche Weiterbildung (f‑bb‑online 03/24). Forschungsinstitut Betriebliche Bildung (f‑bb), Nürnberg. Zum Artikel

Inhalte:
Welchen Stellenwert haben Open Educational Resources (OER) in Zeiten von Künstlicher Intelligenz (KI)? Wie kann die Qualität und die Auffindbarkeit von Bildungsangeboten verbessert werden? Können Lehr- und Lernmaterialien zukünftig sogar automatisch erstellt werden? Dieser Beitrag diskutiert die aktuelle Relevanz von OER in der beruflichen Weiterbildung. Dazu werden die Potenziale von OER und KI für Bildungsanbieter und Unternehmen sowie für Lehrende und Lernende ausgelotet. Ziel ist es, anhand von aktuellen Entwicklungslinien herauszuarbeiten, wie KI und OER sich gegenseitig befördern können. Dabei werden rechtliche Voraussetzungen, technische Möglichkeiten, wirtschaftliche Aspekte und bildungspolitische Strategien thematisiert

Chounta, I.‑A. (2019). A review of the state‑of‑art of the use of machine‑learning and artificial intelligence by educational portals and OER repositories (White Paper). LRE Sub‑Committee. Zum Review (PDF)

Inhalte:
In this report, we provide an overview of the most prominent Art ificial Intelligence (AI) and Machine Learning (ML) practices used in educational contexts focusing on Open Educational Resources (OER) and educational portals aiming to support K -12 education. To that end, we will provide definitions and descriptions of r elevant terms, a short historical overview of Artificial Intelligence (AI) and Machine Learning (ML) in education and an overview of the goals and common practices of the use of computational methods (AI and ML) in educational contexts. We will present the state of the art with respect to the adaptation and use of computational methods in educational portals and OERs and we will discuss the potential benefits and open challenges that may arise from these practices. This report concludes with future directions that could support local structures and directives to move forward with the integration of computational approaches to existing OER and educational portals.

Li, Z., Pardos, Z. A., & Ren, C. (2024). Aligning open educational resources to new taxonomies: How AI technologies can help and in which scenarios. Computers & Education, 216, 105027. Zum Artikel

Inhalte:
Aligning open educational resources (OER) to skill taxonomies is a common task in the education field and helps teachers better locate material that aligns with the standards of their curriculum. When taxonomies change, as they periodically do, re-tagging the increasing mass of open educational resources is needed. The process of manual tagging is, however, exceedingly labor intensive. We propose and evaluate a novel combination of machine learning methods to help automate tagging open educational resources with skills from an existing taxonomy as well as skills from any newly introduced taxonomy. We collected text, image figures, and videos from tens of thousands of educational resources from two major digital learning platforms to answer the research questions of: how effective are machine learning models in automatically updating OER classification to reflect a new taxonomy (RQ1), and which models may be of practical use in different scenarios (RQ2)? Using several taxonomies, including the US Common Core, we find that while full automation is not practically viable, our most generalizable model can reach non-expert human labeling performance requiring only 100 labeled examples and near expert level with 5000. We believe these novel findings may have immediate utility for practitioners and policymakers and better ready the growing landscape of open educational resources for the advent of new taxonomies ahead. We publicly release our pre-trained US Common Core and new taxonomy tagging models, providing guidance on their viability in various real-world scenarios.

OERCamp (2024): OER im Kontext generativer Künstlicher Intelligenz (KI). Ergebnisse der Community-Beratschlagung beim OERcamp in Essen am 20. November 2024. Zu den Ergebnissen

Inhalte:
Im Rahmen des OER-Festivals 2024 in Essen fand am 20. November eine Community-Beratschlagung statt, um gemeinsam aus der Perspektive von Open Educational Resources (OER) auf das Thema generative künstliche Intelligenz (generative KI) zu blicken. Die Diskussion stand im Zeichen des 3rd UNESCO
World OER Congress in Dubai, der sich auf globaler Ebene dem Thema generative KI widmete. Die Teilgebenden des OER-Festivals hatten sich am Vorabend durch eine Live-Schaltung zu den dortigen Themen informiert. Inhaltliche Grundlage der Beratschlagung waren die von der Wikimedia im Frühjahr 2024 veröffentlichten Handlungsempfehlungen „Offene KI für alle“.
Es haben sich rund 100 Personen beteiligt. Die gesammelten Inhalte wurden anschließend mithilfe von KI-Unterstützung aufbereitet, geclustert und in einem kollaborativen Prozess überarbeitet. Das vorliegende Dokument stellt die finale
Version dieser gemeinsamen Arbeit dar, ist jedoch nicht als abschließend zu betrachten. Vielmehr soll es als lebendiges Dokument verstanden werden, mit dem weitergearbeitet und das kontinuierlich verbessert werden kann. Das Ziel war es, die in der OER-Community vorhandenen unterschiedlichen Perspektiven sichtbar zu machen. Vor diesem Hintergrund sind zum Teil widersprüchliche Ideen und Empfehlungen möglich, die jedoch die Vielfalt der beteiligten Personen widerspiegeln.

Ossiannilsson, E., Ulloa Cazarez, R. L., Goode, C., Mansour, C., & Martins Gomes de Gusmão, C. (2024). Artificial Intelligence Use to Empower the Implementation of OER and the UNESCO OER Recommendation. Open Praxis, 16(2), pp. 237–257. Zum Artikel

Inhalte:
Artificial intelligence (AI) has recently been gaining ground, particularly since November 2022, with the introduction of generative tools based on natural language processing and neural network algorithms. These kinds of tools have great potential for creators and users of Open Educational Resources (OER) and the Open Movement itself but they also represent risks. The International Council for Open and Distance Education OER Advocacy Committee (OERAC) developed two workshops to present the role of AI in OER at two international conferences in the fall of 2023. The workshops presented the features, benefits, key challenges, and practical issues related to using AI technologies from professional, ethical, sustainable, and equitable perspectives, while also focusing on the five areas of the UNESCO OER Recommendation. Participants were dynamically engaged in discussions, and documented their ideas in formats that could be used as OER in themselves. The OERAC noted and categorized the results, and developed short summaries and drafts for further work. Finally, drawing on the findings from the workshops, we asked ChatPDF for a second opinion on further suggestions for AI in connection with OER, which in turn related to the five areas of the recommendation. We conclude that, while there is great potential for the use of AI in the context of the Open Movement, there is also a need for professional ethics, equity, and sustainable capacity building, access, inclusion, policy, models, and international collaboration.

Panke, Stefanie. 2024. “Open Educational Resources and Artificial Intelligence for Future Open Education”. Mousaion: South African Journal of Information Studies 42 (1):23 pages . Zum Artikel

Inhalte:
This article explores the intersection of open educational resources (OER) and artificial intelligence (AI), with an emphasis on open pedagogy applications.
The article comprises a document analysis to summarise expert perspectives on generative AI and two open pedagogy course concepts that explore the relationship between OER and AI through a practical lens of contexts, infrastructures, and sample work products. The expert interviews were published in the open-access magazine AACE Review and were conducted by the author to capture the dynamic field of generative AI. The two course concepts offer first-hand experiences of designing and implementing studentcentred, non-disposable assignments by embedding AI tools in open-access book projects.

Qi, Y., & Wang, L. (2024). Learning assessment for open education learners in the era of generative artificial intelligence. In Proceedings of the 2024 10th International Conference on Frontiers of Educational Technologies (ICFET’24) (pp. 38-44). Association for Computing Machinery, New York.  Zum Artikel

Inhalte:
Generative Artificial Intelligence (AI) tools are now used by students to do their learning assessment, thus impairing the value of such learning assessment. For open education learners who aim to just obtain adult education diplomas, particularly, the cost of learning assessment is becoming increasingly low due to the use of generative AI. How to instructional design and assessment for open education learners in the era of generative AI becomes therefore an issue requiring urgent solutions. From such perspectives as the advantages and disadvantages of generative AI, the learning needs and characteristics of open education learners, and the new characteristics of learning assessment with the use of generative AI, this paper examines the dilemma of instructional design and assessment during the age of generative AI, puts forward the ideas and models for the design of homework for open education learners, and makes learning assessment design proposals with respect to renewing the methods of learning assessment, improving the AI literacy of both teachers and students, etc., with a view to offering some insight for the design of homework in the era of generative AI.

Rack, F. (2024). Rechtsfragen zur generativen KI. ABI Technik, 44(1), 39–47. Zum Artikel

Inhalte:
Der Beitrag gibt einen Einstieg in ausgewählte Rechtsfragen der generativen KI. Der Blick richtet sich auf Betreiber, die fremde Materialien einsammeln, um ihre KI-Modelle zu trainieren. Die hierfür geltende gesetzliche Grundlage und ihre Grenzen werden dargestellt. Außerdem wird beleuchtet, was aus Nutzersicht im Hinblick auf fremde Urheberrechte zu beachten ist und wann eigene Urheberrechte beim Einsatz generativer KI entstehen. Auch datenschutzrechtliche Fragestellungen werden thematisiert.

Tlili, A., Agyemang Adarkwah, M., Lo, C. K., Bozkurt, A., Burgos, D., Bonk, C. J., Costello, E., Mishra, S., Stracke, C. M., & Huang, R. (2024). Taming the monster: How can open education promote the effective and safe use of generative AI in education? Journal of Learning for Development, 11(3), 398–413. Zum Artikel

Inhalte:
The development, use, and timely promotion of Open Education (OE) has been effective in addressing myriad educational concerns, including inclusivity, accessibility and learning achievement, among many others. However, limited information exists in the literature concerning how OE could enhance Generative Artificial Intelligence (GenAI), which is receiving extensive interest and criticism at this time. To address this research gap, this study relies on the Open Educational Practices (OEP) framework of Huang et al. (2020) to provide various OEP scenarios that could help to promote and facilitate the effective and safe adoption of GenAI in education. The findings of this study could provide guidelines on how relying on OEP when adopting GenAI could help in ensuring quality education which is the sustainable development goal (SDG 4) of the United Nations (UN).

Tlili, A., & Burgos, D. (2024). Unleashing the power of Open Educational Practices (OEP) through Artificial Intelligence (AI): where to begin? Interactive Learning Environments, 32(10), 6886–6893. Zum Artikel

Inhalte:
The document discusses the evolution of Open Educational Practices (OEP) and the role of Artificial Intelligence (AI) in enhancing OEP. It highlights the challenges and benefits of combining AI with OEP, emphasizing the need for research to understand how AI can reshape open education. The text also calls for papers on topics such as smart open learning environments, personalized OEP, and responsible AI in open education. Overall, the document explores the potential of AI to improve teaching and learning experiences in open educational settings.

UNESCO (2024). Dubai declaration on open educational resources (OER): Digital public goods and emerging technologies for equitable and inclusive access to knowledge. UNESCO. Zur Declaration.
Zur Declaration

Inhalte:
Die Dubai Declaration on Open Educational Resources (OER) wurde von der UNESCO am 20. November 2024 auf dem 3. UNESCO-Weltkongress zu OER in Dubai verabschiedet. Sie baut auf der UNESCO-Empfehlung zu OER von 2019 auf und beschäftigt sich insbesondere mit der Frage, wie Künstliche Intelligenz, digitale öffentliche Güter und offene Bildungsressourcen zusammenwirken können.

Wiley, D. (2024). How generative AI affects open educational resources. Improving Learning. Zum Blogbeitrag

Inhalte:
This is the middle section of my September 19, 2024 presentation, Why Open Education Will Become Generative AI Education. I’m pre-posting some of the presentation content due to the very active conversation the announcement of the presentation has created. Next week I hope to post the first section of the presentation, which outlines the reasons why people who care deeply about affordability, access, and improving outcomes should consider shifting their focus away from OER (as we have understood it for the last 25+ years) and toward generative AI. Or, using the language I introduce below, from “traditional OER” to “generative OER.”
Like the internet before it, generative AI is radically transforming many aspects of society. Generative AI is already having a profound effect on the ways OER are authored, revised, and remixed. And significantly more dramatic impacts are possible if we will reach for them.
Traditional OER

Amin, M. R. M., Ismail, I., & Sivakumaran, V. M. (2025). Revolutionizing education with artificial intelligence (AI)? Challenges and implications for open and distance learning (ODL). Social Sciences & Humanities Open, 11, 101308. Zum Artikel

Inhalte:
Artificial Intelligence (AI) has rapidly progressed in recent years, profoundly impacting various industries. One sector significantly influenced by AI is education, notably Open and Distance Learning (ODL). This transformative technology holds the potential to revolutionize education delivery through personalized learning algorithms, adaptive learning, and intelligent tutoring systems, tailoring educational experiences to individual students. Real-time feedback and customized learning pathways, adapting to students’ abilities and preferences, are among the capabilities AI brings to the educational landscape. However, as AI integration in ODL becomes more pervasive, it raises critical privacy concerns such as data collection and storage, user consent and control, data security and confidentiality, ethical standpoint and consideration, third-party services and integrations, and compliance with regulations. While recognizing the potential benefits of AI in education, it is essential to acknowledge and address the challenges associated with these privacy concerns. By exploring these facets, educators, policymakers, and researchers gain valuable insights into mitigating risks and ensuring responsible AI deployment in ODL. This paper highlights the importance of keeping current with the latest AI advancements in quality education. It provides a comprehensive view of AI’s benefits, challenges, and potential in ODL. Lastly, this paper advocates for a balanced approach that harnesses the transformative potential of AI while safeguarding the privacy and ethical considerations vital for maintaining the integrity of education systems.

Arantes do Amaral, J. A. (2025). Enhancing MOOCs with AI. The AI + Open Education Initiative. Zum Artikel

Inhalte:
This article presents the experiences and insights gained from designing and delivering a series of Massive Open Online Courses (MOOCs) at the Federal University of São Paulo’s Outreach Program, with a focus on integrating artificial intelligence (AI) tools into course development, delivery, and assessment. We followed the Kolb Experiential Learning Cycle to continuously enhance the quality of the courses and adopted a convergent parallel mixed-methods approach for data collection and analysis. Our findings are based on a detailed analysis of the data, reflecting on what was successful, what challenges were encountered, and what improvements could be made. The article also suggests further enhancements, such as more inclusive course design and wider adoption of AI-driven instructional and marketing tools. These findings contribute to the ongoing dialogue about improving MOOCs through AI, offering practical insights for educators in the AI and OER community.

Bucio-García, J. (2025). Creating Open Learning Virtuous Circles: Wikipedia, Generative AI, and Postdigital Literacies in a Social Service Program for Online Students. The AI + Open Education Initiative. Zum Artikel

Inhalte:
The Open Learning Virtuous Circles (OLVC) concept provides a context for integrating generative artificial intelligence (GenAI) tools with open learning practices to create and enhance Spanish Wikipedia content, addressing linguistic barriers faced by students in Mexico’s open and distance education system. Within this framework, we propose NotebookLM for translating and analyzing English academic articles, Wikipedia as a collaborative knowledge platform, and BigBlueButton for interactive learning, to support students in fulfilling their social service graduation requirement while contributing to open knowledge. Designed as a 12-session program, OLVC helps participants to develop Wikipedia editing skills, engage with GenAI to deepen their understanding of academic sources, and collaborate with guidance from Wikimedia Mexico staff. The program aspires to produce 60 new or improved Spanish Wikipedia articles. By fostering openness, inclusivity, and critical reflection, it promotes postdigital literacies and sustainable practices for meaningful engagement with technology in collaborative learning.

Bättig Neusch, E., Corredera Nilsson, E., Flühler, R. & Krüger, N. (2025). Das Kollektiv hinter dem Individuum: OER, KI und die strategische Rolle von Bibliotheken am Beispiel der ZHAW Hochschulbibliothek. Bibliothek Forschung und Praxis, 49(3), 341-351. Zum Artikel

Inhalte:
Die fortschreitende Digitalisierung hat Open Educational Resources (OER) stark vorangebracht, doch eine nachhaltige Integration in den Bildungsalltag bleibt bisher aus. OER sind noch keine Selbstverständlichkeit. Gleichzeitig steht das OER-System vor einer neuen Herausforderung: der Integration von Künstlicher Intelligenz (KI). KI kann eine zentrale Rolle in der Weiterentwicklung von OER und Open Educational Practices (OEP) spielen, bringt jedoch rechtliche Unsicherheiten und komplexe Kooperationsanforderungen mit sich.
Wissenschaftliche Bibliotheken nehmen in diesem Wandel eine Schlüsselrolle ein, indem sie aktive Unterstützung anbieten, um OER von individuellen Initiativen zu breit akzeptierten Standards zu entwickeln. Anhand eines Fallbeispiels der ZHAW Hochschulbibliothek – der Erstellung eines Massive Open Online Course (MOOC) – untersucht der Artikel konzeptionelle und praktische Herausforderungen für Bibliotheken im Kontext von OER und KI. Ziel ist es, die sich verändernde Rolle wissenschaftlicher Bibliotheken in Hochschulen aufzuzeigen sowie Strategien zur Bewältigung rechtlicher Unsicherheiten zu reflektieren.
Im Zentrum steht dabei die kollektive Intelligenz: Diese entsteht durch vertrauensbasierte interne Vernetzung und enge Verbindungen. Bibliotheken sollen nicht nur reagieren, sondern vorausschauend agieren, Bedarfe frühzeitig erkennen und Handlungsoptionen entwickeln. Kollektive Intelligenz und organisatorische Antizipation gelten als zentrale Elemente, um den wachsenden Herausforderungen durch KI zu begegnen.

Clark, H.-B., Dowland, M., Benton, L., Budai, R., Keskin, I. K., Gregory, M., & Hodierne, M. (2025). Auto-Evaluation: A Critical Measure in Driving Improvements in Quality and Safety of AI-Generated Lesson Resources. The AI + Open Education Initiative. Zum Artikel

Inhalte:
Designing AI tools for use in educational settings presents distinct challenges; the need for accuracy is heightened, safety is imperative and pedagogical rigor is crucial.
As a publicly funded body in the UK, Oak National Academy is in a unique position to innovate within this field as we have a comprehensive curriculum of approximately 13,000 open education resources (OER) for all National Curriculum subjects, designed and quality-assured by expert, human teachers. This has provided the corpus of content needed for building a high-quality AI-powered lesson planning tool, Aila, that is free to use and, therefore, accessible to all teachers across the country. Furthermore, using our evidence-informed curriculum principles, we have codified and exemplified each component of lesson design. To assess the quality of lessons produced by Aila at scale, we have developed an AI-powered auto-evaluation agent, facilitating informed improvements to enhance output quality. Through comparisons between human and auto-evaluations, we have begun to refine this agent further to increase its accuracy, measured by its alignment with an expert human evaluator. In this paper we present this iterative evaluation process through an illustrative case study focused on one quality benchmark – the level of challenge within multiple-choice quizzes. We also explore the contribution that this may make to similar projects and the wider sector.

Crudele, F., Raffaghelli, J. E., García-Holgado, A., & Tzallas, A. T. (2025). The Challenge of OER in the Era of AI: A Transnational Intervention. Excellence and Innovation in Learning and Teaching – Open Access, 10(2). Zum Artikel

Inhalte:
The adoption of Open Educational Resources (OER) represents a turning point in democratizing access to knowledge and promoting innovative educational practices. The ENCORE project aims to integrate the use of OER with digital, green and entrepreneurial (DGE) skills through a system based on advanced artificial intelligence technologies. This study analyzes results of a transnational intervention aimed at assessing ENCORE’s impact on increasing OER-related knowledge (OER Knowledge) and explores outcome variability across learner profiles and intervention types. The data collected shows a significant increase in participants’ ability to identify, use and integrate OER in their educational settings, as well as an improvement in their understanding of open licenses and open educational practices. These findings highlight ENCORE’s potential as a tool to facilitate access to OER, emphasizing the importance of course design in shaping learning outcomes and educational practices. ENCORE proves to be a valuable tool for enhancing professional development and promoting innovation. The article reflects implications for instructional design and the adoption of open AI-supported education.

Evenstein Sigalov, S., Cohen, A., & Nachmias, R. (2025). Transforming higher education: A decade of integrating Wikipedia and Wikidata for literacy enhancement and social impact. Journal of Computers in Education, 12, 953–995.
Zum Artikel

Inhalte:
This study examines a decade-long implementation of a course model leveraging Wikipedia and Wikidata as primary educational platforms in higher education. In alignment with the UNs’ SDG 4, this initiative emphasized inclusive, equitable education and lifelong learning opportunities. The study scrutinizes the formulation and deployment of three elective courses, rooted in this model, which were designed to augment students’ academic, digital, collaborative, and communication skills through the creation of Open Educational Resources (OERs), achieving significant social impact—evidenced by over 2000 new and 7000 edited articles, accruing 75 million public views. The research addresses three principal areas: the development and application of the model; course outcomes, including OERs produced, academic achievements, and students’ learning experiences; perceived challenges and benefits from the perspective of both students and faculty. A mixed-methods approach was employed to examine data from 17 iterations, involving 616 participants. Students’ learning experience was extracted from post-course questionnaires completed by 70% (n = 429). Findings demonstrate the role of Wikipedia and Wikidata in fostering knowledge creation, digital and data literacies and critical thinking, with the research contributing to the conversation surrounding Open Educational Practices. Findings include details on incorporating issues of diversity, equity, inclusion (DEI) and knowledge gaps into the curriculum design, and map challenges and benefit for students and faculty. This extensive study offers valuable insights into the effectiveness of embedding OERs in higher education, spotlighting the pedagogical implications and social impact of this approach. It discusses the relevance of this educational strategy in the context of Generative AI technologies.

Gunder, A., Herron, J., Weber, N., Chelf, C., & Birdwell, S. (2025). AI Literacies and the Advancement of Opened Culture: Global Perspectives and Practices. The AI + Open Education Initiative. Zum Artikel

Inhalte:
In an age of significant academic transformation due to the vector of artificial intelligence (AI), this paper explores the intersection of AI literacies and open educational practices (OEP) in fostering an “opened culture” across learning environments and communities. The authors situate AI literacies as a set of interconnected competencies transcending technology use with a propensity for advancing the goals of open pedagogy. Inspired by Stuart Hall’s work in cultural studies and Douglas Belshaw’s work in digital literacies, this qualitative research presents insights from 34 educators from around the world, surfacing the impact of AI on how open educational practitioners are collaborating, creating OER, and building connections to the communities that they serve. Acknowledging broader debates in the field on AI and openness, this research advances the burgeoning discourse on AI in open education, illuminating new pathways for empiricism and advocacy as the field collectively reimagines a more open and inclusive future of learning.

Kimmons, R., Veletsianos, G., & Trust, T. (2025). Judicious AI Use to Improve Existing OER. The AI + Open Education Initiative. Zum Artikel

Inhalte:
As the practical value and ethics of using generative artificial intelligence (AI) in education receive focused attention, open educational resources (OER) provide a mechanism toward an AI future that is more bright and hopeful than the current trajectory. The ecological argument against AI is strong, as training and running complex models requires high levels of resource consumption. Some have argued that the ecological impact of AI alone signifies a social justice, and even existential, threat to humanity by drastically and disproportionately depleting access to water and power and leading to social decision-making that values resources over people . However, such concerns may lose some of their urgency if we shift to more judicious use of these tools (cf., “applied AI” vs. “innovative AI”, ). In this paper, we will discuss what we mean by “Judicious AI Use,” ground our discussion in our own experiences and aspirations in developing and growing an open publishing platform (EdTech Books) and explore how open education can serve as a space for more sustainable and equitable uses of AI to achieve socially valuable goals.

Kopp., M. & Gröblinger, O. (2025): Conditions for AI support of Open Educational Resources at HEIs. Zum Konferenzbeitrag

Inhalte:
Open Educational Resources (OER) and Artificial Intelligence (AI) have been the subject of research for more than two decades and are being tested by early adopters in the context of technology-enhanced teaching and learning. With ChatGPT’s free availability since November 2022, interest in AI has increased, making it necessary to address the support possibilities of AI for the use and creation of OER, further enhancing a learner-centered didactic approach (Ouyang et al., 2022). Tlili et al. (2021) describe several approaches for using AI to support OER creation, including monitoring learning processes, automatically generating metadata, classifying the quality of OER, analyzing learning traces, and personalizing learning processes. However, these approaches to establishing a smart, AI-supported OER environment also face several challenges (Holmes et al., 2019; Kopp et al., 2022), such as didactic issues, legal frameworks and ethical concerns, lack of competencies of the involved stakeholders, and funding. Creating AI-enabled smart OER environments will require significant research and teaching efforts. At the institutional level, HEIs must fulfill necessary conditions, such as preparing stakeholders for smart OER environments, valuing instructional designers, expanding interdisciplinary work, establishing legal certainty, and providing financial and human resources. As a shift from static OER provision to dynamic OER production (Downes, 2019), smart OER environments will support interactivity and community-based creation of OER. However, most teachers need to adapt to this change. Qualification offers are necessary for OER creators to acquire sufficient didactic and technological competencies for the implementation of learner-centered teaching/learning scenarios. HEIs must initiate and continuously accompany the associated cultural change.
Instructional designers have a crucial role in establishing smart OER environments by integrating the didactic component into informational resources. They need a deep understanding of learning science research and educational AI technologies to effectively integrate them in support of learning (Wiley, 2023). HEIs must ensure that their work is valued, and they have sufficient resources.
Developing and operating smart AI-based OER environments require the participation of many different scientific disciplines (including humanities, computer sciences and natural sciences as well as law and social sciences) and collaboration between research and education. HEIs must create necessary conditions for this, such as setting up interdisciplinary working groups and promoting them accordingly. While the legal basis for the creation and use of OER is largely clear, using AI raises questions about copyrights and exploitation rights of AI-produced content (Lalonde, 2023) and ethical concerns related to bias. HEIs must develop legal interpretations to provide staff with legal certainty when using AI systems to produce educational materials. Addressing the benefits and challenges of AI in higher education will concern HEIs even more intensively in the future. They must promote and support research activities related to AI and OER, as well as testing AIInnovating applications in teaching/learning situations also by providing necessary financial and human resources. Those HEIs that create favorable conditions as quickly as possible in this context will not only help shape the future of university teaching but also take a leading role in it.

Miranda, J., Freudenreich, J., Schneider, M., & Glasserman-Morales, L. D. (2025). Design for sustainability of open education resources in the era of AI: A case study. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 20, 244–251. Zum Artikel

Inhalte:
This study addresses the need to ensure the long-term sustainability of Open Educational Resources (OER) in educational environments shaped by Artificial Intelligence (AI). To respond to this challenge, we present the Design for Sustainability of Open Educational Resources (DfS-OER) model. The model is informed by sustainability literature and educational design theory, and it is structured to support practical application. It incorporates five sustainability dimensions: social, economic, environmental, pedagogical, and technological, each connected to a set of design principles. The model was applied in the design of a low-cost, AI-enhanced university course and validated through empirical implementation. Findings from the case study demonstrate that: (a) the model enabled consistent alignment between sustainability objectives and instructional design decisions; (b) the use of AI tools significantly reduced development time, particularly in translation and content generation; (c) inclusive design elements improved learner engagement and accessibility; and (d) the model supports the vision of Education 5.0 by enabling human-centered, scalable, and adaptive learning environments. The DfS-OER model offers a validated pathway for integrating sustainable design practices into digital education systems at scale.

Papazian, P., & Pardos, Z. (2025). Frankenstein Curricula: Stitching Together Open Educational Resources to Generate Dynamic Learning Paths With AI. The AI + Open Education Initiative. Zum Artikel

Inhalte:
In an era where abundant high-quality open educational resources are readily accessible online, the potential for personalized learning through adaptive, dynamic curricula has yet to be fully realized. While a few experienced users may manage to combine diverse sources to create customized learning paths for themselves, most learners are left navigating pre-packaged, often suboptimal learning modules. This paper explores the potential of artificial intelligence to bridge this gap by dynamically generating custom learning paths from OERs, leveraging large language models and advanced data modeling to retrofit existing educational ontologies. We introduce a framework for creating adaptive curricula that are dynamically sequenced based on user needs, behaviors, and learning goals, thereby unlocking new opportunities for personalized education and making OERs accessible and effective for a broader audience.

Pietrusky, S. (2025). KI und offene Bildung – Bildung ohne künstliche Barrieren. Mit KI-Kompetenz und Open Source für mehr Gerechtigkeit bei der individuellen Kompetenzentwicklung. Zum Artikel

Inhalte:
Die zunehmende Verbreitung von KI im Bildungsbereich birgt Chancen und Herausforderungen. Während proprietäre Systeme den Zugang einschränken, bieten Open-Source-Modelle wie Llama Alternativen zur Förderung der Chancengleichheit. Der Beitrag stellt mit Open WebUI und dem Individual Educational Chatbot (IEC) zwei Ansätze vor, mit denen sich Lernassistenten lokal betreiben und individuell anpassen lassen. Zudem wird auf relevante KI-Kompetenzen und den Einfluss von Parametern auf LLM-Antworten eingegangen. Die Integration von Open-Source-KI in Bildungseinrichtungen erfordert didaktische Konzepte und kontinuierliche Weiterbildung, um die Technologie effektiv zu nutzen und Bildungsungleichheiten zu reduzieren.

Rampelt, F., Ruppert, R., Schleiss, J., Mah, D.-K., Bata, K., & Egloffstein, M. (2025). How do AI educators use open educational resources? A cross-sectoral case study on OER for AI education. Open Praxis, 17(1), 46–63. Zum Artikel

Inhalte:
Artificial Intelligence (AI) literacy is essential for society as a whole. While general frameworks and resources to support self-directed learning on AI are widely available, research on how to support AI educators, particularly those without AI expertise (non-experts), using external materials and resources is relatively scarce. This article explores the potential of open educational resources (OER) to enhance AI education,
with a specific focus on the requirements and practices of AI educators. Through a case study of the AI Campus learning platform, the article examines how educators from diverse sectors such as school education, higher education and professional education utilise OER for AI education. The study aimed to identify patterns of OER usage, AI educator motivations and the sector-specific integration of OER into teaching practices. A survey study of 260 educators from Germany, Austria, and Switzerland using AI Campus content revealed that educators prefer smaller, modular OER formats and value suitable, high-quality and accessible content. The reputation of the person or institution that created the OER content does not seem to play a major role. Sector-specific differences could be observed in particular with regard to full online courses, face-to-face learning scenarios and the AI learning objectives of an educator. By focusing on educators’ perspectives, the study provides insight into how AI education can be strengthened across sectors through the use of OER materials and ultimately benefit learners through suitable, high-quality content and adequate AI learning scenarios.

´Schlotfeldt, A. (2025). 10 Jahre HOOU – 10 Fragen zu KI‑Output, Urheberrecht & OER. Hamburg Open Online University (HOOU). Zum Beitrag

Inhalte:
In der Broschüre „10 Jahre HOOU – 10 Fragen zu KI-Output, Urheberrecht und OER“ werden zehn häufig gestellte Fragen rund um KI-generierte Inhalte, deren Nutzungsmöglichkeiten, Kennzeichnungspflichten sowie KI-Output als Bestandteil von Open Educational Resources umfassend beantwortet. Die Broschüre greift gezielt zehn Fragen auf, die in Urheberrechts- und OER-Workshops sowie -Beratungen häufig gestellt wurden. Sie bietet praxisnahe Antworten auf diese relevanten Fragen und sensibilisiert die Leserschaft für rechtliche Sonderfälle, die bei der KI-Nutzung bislang möglicherweise übersehen wurden. Sämtliche Antworten auf die FAQ stehen sowohl in einer knappen Übersicht als auch in einer ausführlichen Fassung zur Verfügung.

Shilibekova, A. (2025). Addressing Challenges in Faculty Professional Development: UDL Training through AI-Enhanced OER in a Non-English Context. The AI + Open Education Initiative. Zum Artikel

Inhalte:
In an increasingly interconnected world, education must evolve to address the diverse needs of learners, particularly where linguistic, cultural, and technological barriers limit access to high-quality, inclusive education. This rapid response paper presents an approach to these challenges through an AI-powered Universal Design for Learning (UDL) course developed as an Open Educational Resource (OER) for Kazakh-speaking educators at Atyrau State University in Kazakhstan. The course leverages advanced AI tools to create culturally relevant and accessible learning materials that effectively bridge these barriers.
By integrating UDL principles into both content and design, this approach introduces a meta-layer of UDL that enhances inclusivity for a diverse range of learners. Utilizing the Successive Approximation Model (SAM), the course underwent iterative refinement, achieving a 95% completion rate among participants. Key findings highlight the significance of culturally aligned content, the complexities of AI-driven localization, and the scalability of AI-enhanced OER for professional development. This paper illustrates how AI-powered OER can foster equity and inclusivity in education, offering a replicable model for transforming professional development in diverse and underserved educational contexts globally.

Shyrokova, M. (2025). Artificial intelligence in open educational resources (OER) for doctoral education. In Advances in Computational Intelligence and Robotics (pp. 237–264). IGI Global. Zum Artikel

Inhalte:
This chapter examines the integration of Artificial Intelligence (AI) into Open Educational Resources (OER) for doctoral education, focusing on the Ukrainian context. It explores how AI can enhance the accessibility, adaptability, and pedagogical effectiveness of OER, especially during crises. Drawing on empirical research, it highlights both the potential and limitations of AI in supporting doctoral training. Key themes include the uneven awareness and use of OER among students and educators, the role of AI in content personalization and translation, and the ethical challenges surrounding bias, trust, and academic integrity. The chapter introduces the concept of an AI-driven OER ecosystem, where intelligent tools automate content curation, semantic tagging, and multilingual access, while human actors maintain oversight and contextual relevance. The chapter frames AI as both a tool for human augmentation and a force requiring vigilant oversight, advocating for a balanced approach that pairs technological innovation with transparent governance and critical data literacy.

Tawileh, W. & AL-Smadi, M. (2025). From Open Educational Resources to Open Pedagogy – Navigating the AI-Powered Future of Digital Learning. In U. Lucke, S. Stieglitz, F. Uebernickel, A.-L. Lamprecht, M. Klein (Hrsg.): INFORMATIK 2025, 1155-1160. Zum Conference Paper

Inhalte:
In the rapidly evolving digital education landscape, the integration of Open Educational Resources (OER) and Artificial Intelligence (AI) presents unprecedented opportunities to enhance learning outcomes and accessibility. This paper presents a work in progress at Qatar University, a leading traditional institution in the Arab region exploring innovative educational approaches along its digital transformation journey by analyzing existing frameworks, models, and best practices of open pedagogy to develop and validate a context-aware AI-enabled open digital learning framework. Informed by established Open Educational Practices (OEP) and leveraging AI capabilities to address challenges facing OER adoption, including limited awareness and support, need for professional development, lack of quality localized OER, difficulty in fostering collaboration, and financial scarcity, the intended framework encompasses five components: culture and governance, capacity building, quality assurance, impact assessment, and sustainability. Multiple facilitating elements like innovative technologies, contextual alignment, accessibility, adaptability, scalability, and synergies should be incorporated in further developments.

twillo (2025). KI und OER: Einblicke in aktuelle Nutzung und Herausforderungen. Ergebnisse der twillo-Studie 2025. Blogartikel. Zum Blogartikel

Inhalte:
Generative KI verändert zunehmend die Art, wie offene Lehrmaterialien entstehen. Eine twillo-Erhebung aus dem Frühjahr 2025 zeigt, wie Hochschulen, Lehrende und aktive Nutzer*innen KI bereits einsetzen, wo Potenziale liegen und an welchen Stellen Unsicherheiten bestehen. Der Beitrag fasst zentrale Ergebnisse zusammen und ordnet sie für die OER-Praxis ein.

Wannemacher, K., Stein, M., Seyfeli-Özhizalan, F. (2025c). Perspektiven der KI-Nutzung für die Open Education. Erkenntnisse der Begleitforschung im niedersächsischen twillo-Verbund, in: Lucke, U., Stieglitz, S., Uebernickel, F., Lamprecht, A.-L., Klein, M. (Hrsg.): INFORMATIK 2025. The Wide Open: Offenheit von Source bis Science. Bonn: Gesellschaft für Informatik (Lecture Notes in Informatics (LNI), Bd. 366), S. 1095-1104. Zum Artikel

Inhalte:
Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements; (2) decomposes skills into learning topics; (3) collects a variety of open online educational resources that address those topics; (4) checks the quality of those resources and topic relevance with three intelligent prediction models; (5) helps learners to set their learning goals towards their desired job-related skills; (6) recommends personalized learning pathways and learning content based on individual learning goals; and (7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by means of a pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal recommendations provided by eDoer to acquire knowledge of basic statistics, attained higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported.

Celik, B., & Şahin, F. (2026). Empowering teacher training with AI: Factors shaping the development and use of open educational resources. International Journal of Educational Management, 39(1), 1–20. Zum Artikel

Inhalte:
The integration of artificial intelligence (AI) into open educational resource (OER) development presents new opportunities for enhancing educational experiences. However, the factors shaping pre-service teachers’ adoption of AI-assisted OER development still unexplored. This study examines the key determinants of pre-service teachers’ (psts) behavioral intentions to develop and use AI-assisted OER, drawing from Uses and Gratifications Theory and related constructs. Data were collected from 240 psts. Participants received 10 weeks training on AI-supported OER development. Data analysis was conducted performing structural equation modeling and bootstrapping. The findings reveal that compatibility, enjoyment, task efficiency, and information seeking significantly influence both trust and behavioral intention. However, trust did not. Social factors yielded mixed results. While social influence positively affected behavioral intention, it did not significantly impact compatibility or trust. Similarly, social interaction fostered trust but did not directly influence behavioral intention. While peer and instructor opinions encourage adoption, they do not necessarily enhance AI’s trustworthiness or suitability for educational tasks. The study contributes to the theoretical understanding of AI acceptance in education by refining existing adoption models with social and cognitive factors. Findings suggest that training programs should emphasize AI’s alignment with teaching tasks and focus on enhancing user enjoyment to drive adoption.

Duan, C. (2026). The evolution of open educational practices: A comparative analysis of models and a new framework for the AI era. Smart Learning Environments, 13, Article 15. Zum Artikel

Inhalte:
This research provides a systematic review and comparison of 14 existing Open Educational Practices (OEP) models and frameworks. Although OEP has its origins in Open Educational Resources (OER), the theoretical conception of OEP remains disparate and heterogeneous. In order to address this issue, the study operationalizes the holistic view of the models by providing a multi-dimensional comparison of these models across their focus, key players and stakeholders, core processes and practices, values and technology orientations. The empirical results point at the holistic evolutionary trajectory for OEP: a Resource Dimension focused on access and creation. A Teaching Dimension focuses on open pedagogy and learner agency. A Technology Dimension focuses on technology, shifting roles from mere tools to integral pieces of educational infrastructure. In line with this development, this study proposes a novel AI-empowered Open TPACK Framework as the next stage of evolution of OEP, where AI is conceptualised not as a tool but as the infrastructure of Content, Pedagogy, and Technology Knowledge (TPACK). This study asserts that the transition from “Resource Access” to “AI Empowerment” indicates a meaningful change in the concept of Open Education. This research provides direction and guidance to educators and policy-makers in effective and meaningful OEP in an algorithmic age, highlighting the need for human agency within clever ecosystems to create sustainable learning environments.

Ahmadfard, M. (2026). Advancing open learning through interactive open educational resources. Discover Education, 6. Zum Artikel

Inhalte:
This paper provides a critical review of Interactive Open Educational Resources (iOER) as a next-generation direction within the open education movement. While traditional OER have expanded access and reduced costs, their static formats often limit engagement and adaptability. iOER address these gaps by incorporating interactive technologies, multimedia, and adaptive learning systems to support dynamic, personalized, and student-centered learning. Drawing on constructivist, active learning, and sociocultural perspectives, and informed by Universal Design for Learning and Cognitive Load Theory, this review synthesizes core design principles for iOER, including user-centered design, scalability, and alignment with learning outcomes. It categorizes common interactive elements (e.g., simulations and embedded assessments) and synthesizes evidence on their effects on motivation, engagement, and equity. The paper also identifies implementation challenges—such as digital access, institutional readiness, and faculty development—and provides strategic recommendations for addressing them. Finally, it explores how artificial intelligence can further personalize iOER and enhance learning analytics, while underscoring the ethical and practical considerations involved. Overall, the paper proposes a conceptual and practical framework to guide educators, researchers, and policymakers in advancing equitable, innovative, and impactful open education through iOER.

Tlili, A., Farrow, R., Bozkurt, A., Amiel, T., Wiley, D., & Downes, S. (2026). The double-edged sword: Open educational resources in the era of generative artificial intelligence. Journal of Applied Learning & Teaching (JALT), 9(1). Zum Artikel

Inhalte:
The integration of Generative Artificial Intelligence (GenAI) into the Open Educational Resources (OER) landscape represents a paradigmatic shift, transforming OER from static content into a dynamic, algorithmic infrastructure. While GenAI promises to democratize content creation and accelerate localization, it simultaneously introduces profound ethical and epistemic risks. This commentary, in this regard, adopts a speculative-critical methodological approach to interrogate the „double-edged“ nature of this transition. We analyze several emerging tensions: the ontological crisis of human authorship, which challenges traditional copyright frameworks; the risk of „openwashing“ where proprietary models appropriate the language of the open movement; the potential for automated translation to amplify Global North epistemic biases; and the paradox of hallucination where OER serves as both a corrective ground truth and a potential casualty of remix culture. By comparing and contrasting the optimistic imaginaries of AI-enhanced access against critical perspectives on data surveillance and commodification, this paper argues that the binary definition of „openness“ is no longer sufficient. We conclude that ensuring equity in the AI era requires a transition from open content creation to the stewardship of „white box“ technologies and transparent digital public goods.

Wannemacher, K., Seyfeli-Özhizalan, F., Stein, M. (2026). Perspektiven einer KI-unterstützten
offenen Bildungspraxis. Eine Interviewstudie im Rahmen des niedersächsischen twillo-
Verbundes. HIS-HE:Forum 1 | 2026.  Zur Studie

Inhalte:
Die vorliegende multiperspektivische Erhebung untersucht die Nutzung von Künstlicher Intelligenz (KI) im
Kontext der Open Education an Hochschulen im deutschsprachigen Raum. Sie basiert auf einer qualitativen
Befragung unterschiedlicher Akteursgruppen: OER-Lead-User und Early Adopter, Vizepräsident:innen für
Lehre und Studium, Studiendekan:innen niedersächsischer Hochschulen sowie Nutzer:innen der OER-
Plattform twillo. Ziel war es, ein umfassendes Bild der aktuellen Praxis, der strategischen Ausrichtung sowie
der Potenziale einer künftigen KI-Nutzung in der Entwicklung, Anpassung und Nutzung offener
Bildungsmaterialien (OER) zu gewinnen.

Wang, V. (2026). AI‑driven content creation. In AI Applications and Pedagogical Innovation (pp. 123–150). IGI Global. Zum Artikel

Inhalte:
This chapter discusses how AI-driven content creation is transforming the development of learning resources, enabling educators to create, adapt, and share Open Educational Resources (OERs) more efficiently and inclusively. It highlights the use of AI technologies in lesson planning, instructional material development, and multimedia content generation. The chapter also explores key considerations such as intellectual property rights, ensuring pedagogical quality and content accuracy, and addressing bias, misinformation, and credibility issues in AI-generated OERs. Furthermore, it proposes approaches for fostering inclusivity and diversity through AI-supported educational resources while emphasizing the importance of balancing AI automation with human expertise. By addressing these opportunities and challenges, the chapter encourages educators to thoughtfully integrate AI tools into OER initiatives to enhance educational access, equity, and innovation while maintaining educational integrity.

Weimer, V., Grimm, S., Orr, D., & Rasulzade, S. (2026). Open Educational Resources (OER) in Deutschland: Teil eines Open Science Kulturwandels. Zeitschrift für Erziehungswissenschaft. Zum Artikel

Inhalte:
Dieser Beitrag untersucht OER als Teil der Open Science-Bewegung und verfolgt ihre Entwicklung in DE aus der Perspektive des kulturellen Wandels. Unter Anwendung von Noseks Strategie für Kulturwandel analysieren wir, wie die Implementierung von OER eine koordinierte Entwicklung über mehrere Dimensionen hinweg erfordert. Anstatt einer linearen Entwicklung zu folgen, zeigt die deutsche OER-Entwicklung ein komplexes Zusammenspiel dieser Elemente in drei konsekutiven Phasen. Jede Phase zeigt das Zusammenspiel zwischen den Idealen offener Bildung, organisatorischen Veränderungsinitiativen und rechtlichen Rahmenbedingungen innerhalb des dezentralen deutschen Bildungssystems. Während bedeutende Fortschritte in Richtung Institutionalisierung gemacht wurden, bestehen die Herausforderungen der praktischen Umsetzung fort. Wir argumentieren, dass Künstliche Intelligenz einen entscheidenden Fortschritt darstellt, da sie speziell auf die Barrieren der Benutzerfreundlichkeit eingeht und die Nutzung von OER für Pädagogen erleichtert. Die Integration von KI führt jedoch zu neuen Überlegungen, die in bestehenden OER-Rahmenwerken berücksichtigt werden müssen. Der Beitrag veranschaulicht, wie kulturelle Veränderungen in der Bildungspraxis eine Abstimmung zwischen philosophischen Prinzipien, praktischen Werkzeugen und politischer Unterstützung erfordern.

Fischer, G. (2023): KI und OER: Wie gut passen sie zusammen? Zum Artikel

Inhalte:
KI-Technologien laden dazu ein, wie durch Zauberhand neue Texte, Bilder oder Musik generieren zu lassen. Unter bestimmten Umständen eignen sich KI-Schöpfungen gut als OER. Doch die Entwicklung ist dynamisch. Nicht auf alle urheberrechtlichen Fragen gibt es derzeit Antworten.

Weitere Bereiche der Themenwelt

Im Folgenden finden Sie noch einmal die Übersicht über die verschiedenen Bereiche der Themenwelt:

Übersicht
Chancen und Potenziale
Verhältnis von OER & KI
Rechtliche Aspekte
Herausforderungen
Praktische Anwendung
Kompetenzen
Literatur