Research Insights #7: Open Educational Resources & Generative AI Part 1

What is the emerging literature on open education and generative AI?

Apr 09, 2024

I’m taking a short break from the focus on research focused on students and generative AI to do two posts (today and tomorrow) on Open Education and Generative AI.

My inspiration for this is an event tomorrow. If you are around on Wednesday (April 10, 2024, 3pm EDT/ 12pm PDT), I will be moderating a panel on this particular topic with two amazing colleagues, Anna Mills and Peter Shea. The talk is part of the Community College Consortium for Open Educational Resources (CCCOER) which you should definitely check out if OER is your jam. You can get the full details for this panel here: AI & OER: Redefining Education?

My interest in this work is pretty strong as I’ve been thinking about and developing open educational resources for over a decade (all this writing and the writing on my other blog as well as my YouTube content is all CC licensed) and of course, my dissertation is focused on the open access question for research. Additionally, last year, I had the honor to work with Maha Bali and Anna Mills to coauthor this piece: How do we respond to generative AI in education? Open educational practices give us a framework for an ongoing process by the Journal of Applied Learning and Teaching.

OSSIANNILSSON, E., CAZAREZ, R. L. U., GOODE, C., MANSOUR, C., & DE GUSMÃO, C. M. G. Artificial Intelligence Use to Empower the Implementation of OER and the UNESCO OER Recommendation.

Generative AI summary

Chat log

Part 1 - Study's Findings and Educational Implications
The document provides an insightful analysis of the use of Artificial Intelligence (AI) to empower the implementation of Open Educational Resources (OER) aligned with the UNESCO OER Recommendation. The main findings highlight that AI and OER are rapidly advancing, emphasizing the necessity to integrate these technologies within the educational framework effectively. The analysis, grounded in data from international conferences and workshops, demonstrates the thematic alignment with the five areas of the UNESCO OER Recommendation, offering a nuanced understanding of AI's role in enhancing the capacity for creating, accessing, re-using, adapting, and redistributing OER.
The educational implications of these findings are profound. For higher education, teaching, and learning, this suggests a pivot towards integrating AI to bolster the OER ecosystem. Specifically, the findings point to the potential of AI in training stakeholders in OER creation and dissemination, identifying and sharing resources, and maintaining technology. For instance, the emphasis on developing supportive policies and professional development as strategies to navigate AI and OER integration is pivotal. This aligns with the broader educational narrative emphasizing AI's role in personalizing learning, ensuring accessibility, and enhancing the overall quality and reach of educational resources.
Part 2 - Strategies, Methodologies, and Theoretical Perspectives
The document articulates a clear methodology grounded in Qualitative Research, employing tools like 'Wall of Wonder' and Padlet for data collection and analysis. This approach emphasizes the interactive and participatory nature of the research, ensuring the authenticity of the participants' inputs. The thematic analysis, particularly the use of AI tools like ChatGPT and ChatPDF, underscores a nuanced methodological approach to dissecting large datasets, illustrating AI's utility in extracting and synthesizing educational insights.
From a strategic viewpoint, the focus on professional development, accessibility features, and ethical AI use offers a roadmap for integrating AI within the OER framework effectively. These strategies are not only pivotal for enhancing the quality and reach of OER but also underscore the necessity of a principled approach to AI integration, ensuring educational equity and inclusivity.
The theoretical underpinnings of the study are evident in its alignment with the UNESCO OER Recommendation, grounding the discussion in a globally recognized framework. This not only lends credibility to the analysis but also situates the findings within a broader discourse on open education and AI's role in shaping future educational landscapes.
In conclusion, the document offers a comprehensive and nuanced exploration of AI's role in enhancing the OER ecosystem. It provides valuable insights and practical strategies for educators, policymakers, and stakeholders in higher education, emphasizing a principled, inclusive, and forward-looking approach to integrating AI within the educational domain.
Part 3 - Gaps, Challenges, and Limitations:
The document presents a robust exploration of AI's integration into OER within the context of UNESCO's OER Recommendation. However, there are some gaps, challenges, and limitations that need attention:
Gaps in Practical Implementation: While the study outlines theoretical perspectives and strategic approaches, there's a gap in detailing the practical implementation of these strategies within diverse educational settings. The document doesn't provide in-depth case studies or empirical evidence on the effective integration of AI in OER at an institutional level.
Challenge of Ensuring Equity: The emphasis on AI and technology-driven solutions could inadvertently widen the digital divide. The document touches on inclusivity and equity but does not delve deeply into strategies to ensure that AI-enhanced OER benefits all learners, especially those in resource-constrained environments.
Limitation in Addressing Ethical Concerns: The study mentions ethical considerations of AI in education but lacks a comprehensive exploration of these issues. AI's potential biases, privacy concerns, and the implications of data-driven decisions in educational contexts require more detailed scrutiny.
Challenge in Faculty Training and Professional Development: The document highlights the need for professional development but does not address the specific competencies that educators need to develop to effectively integrate AI into their teaching practices.
Educational developers should consider these aspects critically:
Practical Implementation: They should work on creating actionable plans that detail how AI can be integrated into OER development and usage, with clear steps and measurable outcomes.
Equity and Access: Developers should prioritize strategies that ensure the equitable distribution of AI-enhanced OER resources, especially targeting underrepresented or disadvantaged groups.
Ethical Framework: It's crucial to develop a robust ethical framework that guides AI use in education, ensuring that these technologies are used responsibly and transparently.
Focused Professional Development: Tailored training programs are necessary to equip educators with the skills to leverage AI in OER, emphasizing ethical usage, pedagogical integration, and technical proficiency.
Part 4 - Application to Educational Development:
The insights from the document offer valuable directions for educational developers and faculty in harnessing AI to enhance OER:
Capacity Building through AI: The findings underscore AI's role in streamlining OER creation and dissemination. Educational developers can use these insights to design AI-powered tools that assist educators in developing, curating, and updating OER, making these resources more dynamic and responsive to learner needs.
Policy Development: With the document's emphasis on developing supportive policies, educational developers can collaborate with policymakers to craft guidelines that encourage the ethical and equitable use of AI in OER, ensuring that these innovations align with institutional goals and educational standards.
Professional Development: The study's focus on professional development highlights the need for ongoing training for educators. Developers can use these insights to create comprehensive training modules that address AI's pedagogical integration, ethical considerations, and technical aspects, empowering faculty to effectively use AI-enhanced OER in their teaching.
International Collaboration: Given the emphasis on international cooperation, educational developers can initiate and foster cross-institutional projects that leverage AI to create, share, and improve OER, enhancing global educational resources and fostering a more collaborative educational environment.
By adapting these recommendations, educational developers can play a pivotal role in advancing the use of AI in OER, driving innovation in teaching and learning, and contributing to the broader educational community's development.

My Commentary

On the one hand, this article does open up a line of thinking I know folks are considering about how generative AI could be a fantastic boon to the OER movement. That the content can be more easily created and then edited by humans is very tempting given how much time OERs take to create. I'm intriguied by the use of a "Wall of Wonder" and am now starting to explore/learn more about that. As I got to the limitations, many were already swirling in my head. This is great in theory--the alignment is on target but that still requires a great deal of other considerations as the article mentions. It will still require work and another set of skills and guidance for those who want to create using generative AI (e.g. critical AI literacy) Additionally, I do wonder if the article addresses the concerns about the weird tension with AI and OER. OER is a permission system based upon providing credit about how one's work has been created. AI on its own doesn't do that. So to create OERs from AI leaves a sour-taste in many active voices in OER (self-included) because credit for the original work is not evident.

Share AI + Education = Simplified

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

Generative AI summary

Chat log

Part 1: Study's Findings and Educational Implications
Main Findings of the Study
The study explores the integration of Artificial Intelligence (AI), specifically ChatGPT, into Open Educational Resources (OER) to enhance the quality and accessibility of educational materials. Key findings include:
Diverse Perceptions: Faculty and students have differing views on AI's role in education, with students being more optimistic about AI's benefits.
Familiarity and Usage: There is a noticeable gap between students and faculty regarding familiarity and usage of AI tools, with students being more engaged.
AI's Educational Impact: AI tools, particularly ChatGPT, can support the curation and creation of OER, offering a more dynamic and interactive learning environment.
Ethical and Effective Use: The study emphasizes the importance of educating students on ethical and effective AI use, suggesting that AI tools should complement rather than replace traditional learning methods.
Educational Implications
Enhanced Learning Resources: AI can provide dynamic and customizable OER, making learning more accessible and tailored to individual needs.
Pedagogical Shifts: Educators should consider integrating AI tools into their teaching methodologies, fostering a more interactive and student-centered learning experience.
Digital Literacy: The rise of AI necessitates a focus on digital literacy, ensuring students can critically engage with and ethically use emerging technologies.
Collaborative Learning: AI can facilitate collaborative learning environments, allowing students to work together on content curation and critical analysis.
Part 2: Strategies, Methodologies, and Theoretical Perspectives
Strategies and Methodologies
Survey Analysis: The study utilized surveys to gather perceptions from faculty and students, providing insights into the community's readiness and concerns regarding AI integration.
Iterative AI Interaction: Students are encouraged to interact with AI tools in a controlled manner, refining their queries based on previous outputs, which fosters critical thinking and understanding.
OER Curation: Utilizing AI for OER curation involves students actively engaging in the selection and evaluation of educational content, enhancing their learning autonomy and critical analysis skills.
Theoretical Perspectives
Constructivist Approach: The study aligns with a constructivist learning theory, where students construct knowledge through active engagement with content, facilitated by AI.
Open Pedagogy: Emphasizing open pedagogy, the study advocates for a participatory learning environment where students are co-creators of their educational content, supported by AI tools.
Technological Acceptance Model: The study indirectly references the technological acceptance model, examining factors that influence users' willingness to embrace AI in educational settings.
Enhancing Educational Value
Critical Engagement: By actively involving students in the curation and analysis of OER content with AI, they develop critical thinking and digital literacy skills.
Adaptive Learning: AI can provide personalized learning pathways, allowing students to explore content that aligns with their interests and needs, thereby enhancing engagement and retention.
Faculty Development: Educators are encouraged to familiarize themselves with AI tools, integrating them into their pedagogy to foster a more interactive and inclusive learning environment.
In conclusion, the integration of AI into OER represents a significant shift in educational methodologies, emphasizing the need for critical engagement, digital literacy, and adaptive learning strategies to fully harness the potential of these technologies in enhancing teaching and learning in higher education.
Part 3: Gaps, Challenges, and Limitations
Gaps and Challenges Identified
Perception Disparity: The study highlights a significant disparity in the perceptions of AI between students and faculty. While students are more optimistic, faculty members exhibit reservations, primarily concerning AI's potential to undermine critical thinking and writing skills. This gap underscores the need for a more nuanced understanding and integration of AI in pedagogical strategies.
Ethical and Responsible Use: The study touches upon the need for ethical guidance in using AI, but it does not delve deeply into the frameworks or mechanisms to ensure ethical compliance. The risk of plagiarism, data privacy concerns, and the reliance on AI without understanding its limitations are challenges that require more comprehensive strategies.
Faculty Development: There's a noticeable lack of emphasis on professional development for faculty to effectively integrate AI into their teaching. While the study suggests faculty need to become more familiar with AI, specific pathways for achieving this are not adequately explored.
Assessment of Learning Outcomes: The document mentions using AI for enhancing educational experiences but does not provide a detailed analysis of how AI integration impacts learning outcomes. Measuring the efficacy of AI-augmented OER on student performance is crucial for its justified inclusion in educational resources.
Implications for Educational Developers
Educational developers should facilitate dialogues and workshops to bridge the perception gap between students and faculty, promoting a shared understanding of AI's potential and pitfalls.
There is a need to develop clear guidelines and ethical frameworks for AI use in education, ensuring that all stakeholders are aware of and adhere to best practices.
Professional development programs should be designed to equip faculty with the necessary skills and knowledge to integrate AI into their pedagogy effectively.
Implementing and evaluating AI-based interventions should include robust assessment mechanisms to track and analyze their impact on student learning outcomes.
Part 4: Application to Educational Development
Relevance to Educational Developers and Faculty
Curriculum Design: Educational developers can use insights from the study to design curricula that incorporate AI tools in a manner that enhances learning without compromising on critical thinking and writing skills. This might include integrating AI-assisted activities that require students to critically engage with and refine AI-generated content.
Faculty Training: The study's findings underscore the importance of faculty training in AI. Educational developers should create training modules that familiarize faculty with AI tools, focusing on their pedagogical applications and limitations to encourage informed and responsible use.
Student Engagement: Insights from the study can guide educational developers in creating strategies that leverage AI to increase student engagement and participation. For example, using AI to create interactive and adaptive learning materials can cater to diverse learning preferences and needs.
Adaptation and Application of Recommendations
Ethical Use of AI: Educational developers should establish a set of ethical guidelines for AI use within their institutions, informed by the study's insights on the potential misuses of AI in educational contexts.
Open Pedagogy Initiatives: The study's emphasis on using AI for curating OER content aligns with open pedagogy principles. Educational developers can spearhead initiatives that involve students in creating and refining educational content using AI, promoting a sense of ownership and active learning.
Research and Evaluation: The study highlights the need for further research on AI's impact on learning. Educational developers should advocate for and participate in research efforts to evaluate the effectiveness of AI tools in education, ensuring that their adoption is evidence-based.
In conclusion, the document provides a foundation for understanding the opportunities and challenges of integrating AI in education. Educational developers have a critical role in translating these insights into actionable strategies that enhance teaching and learning while addressing the ethical, practical, and pedagogical challenges presented by AI technologies.

My Commentary

Part 1 of this review offers nothing that I don't think is a problem on its own. They are all potential use cases that make a lot of sense on face value and are connected with the ethical approach that includes tool literacy. Though I'm really curious about what "curation of OER" means here. The article doesn't go into that much (only uses "curate" 5 times in the article). It appears that it's framing it that students use OER in an AI to improve a more relatable text and also, to curate answers from AI to create course resources. I can get behind that but I do wish it was a bit more using AI to curate the amazing work that has already been done in OER and put them in front of students and faculty.

I do really find it appealing of creating content (a course text) that leans on generating materials from AI that the students then have to certified is true without AI--that could be really interesting to play out (and know folks have been doing this in small ways--but what about an entire course?).

One thing not noted here is that the article has an appendix that includes prompts that it tested out as part of the process. Worth checking out and would love to see more of these in articles in the future.

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.

Generative AI summary

Chat log

Part 1 - Study's Findings and Educational Implications
Main Findings:
The paper by Aras Bozkurt critically examines the transformative potential of integrating generative AI with Open Educational Resources (OER) and Open Educational Practices (OEP). The study identifies the emergence of AI in content creation, highlighting its capacity to generate human-like content and its implications for OER and OEP. Key findings include the capabilities of generative AI in automatic content generation, resource curation, and enhancing collaborative learning. However, it also raises concerns about the quality and reliability of AI-generated content, data privacy, and equitable access to AI technologies.
Educational Implications:
For higher education, these findings imply a paradigm shift in how content is created, curated, and disseminated. AI's capacity to generate educational materials can lead to a more dynamic and responsive educational environment. For instance, AI-generated textbooks and learning materials can be continuously updated, tailored to individual learner's needs, and made more interactive. However, educators and institutions need to address the challenges posed by AI, such as ensuring the credibility of AI-generated content and maintaining data privacy. The question of authorship and ownership in the context of OER/OEP is particularly pressing, requiring clear guidelines and ethical considerations.
Part 2 - Strategies, Methodologies, and Theoretical Perspectives
Strategies and Methodologies:
The document outlines several strategies and methodologies facilitated by generative AI, including automated content generation, resource curation, and the facilitation of personalized learning paths. The concept of prompt engineering emerges as a novel methodology, where educators can guide AI to produce desired educational content, thus fostering a co-creative process between humans and AI. This co-creation is envisioned not just as a production of content but as a dialogue that enhances educational practices.
Theoretical Perspectives and Impact:
The integration of generative AI into OER and OEP is underpinned by theoretical perspectives that view technology as an augmentative tool rather than a replacement for human educators. The paper encourages a view of AI as a collaborator in the educational process, enhancing the capabilities of educators and students. This aligns with constructivist theories where learning is seen as a collaborative and dynamic process. By leveraging AI, the field of education can adopt a more nuanced and sophisticated approach to teaching and learning, one that is continuously evolving and adapting to the needs of learners.
In conclusion, this paper highlights a significant shift in the educational landscape, driven by the capabilities of generative AI. While there are substantial benefits, such as enhanced resource availability and personalized learning, there are also critical challenges that need addressing. As educational developers, it's crucial to engage with these developments critically, ensuring that the integration of AI into education enhances rather than detracts from the learning experience.
Part 3 - Gaps, Challenges, and Limitations
Identification of Gaps and Challenges:
Quality and Reliability of AI-Generated Content: The document raises concerns about the reliability and quality of content generated by AI. This is a significant gap as there is a need for mechanisms to ensure the accuracy and pedagogical value of AI-generated materials.
Data Privacy and Ethical Concerns: While the paper touches on data privacy and equitable access to AI technologies, it could delve deeper into the ethical implications, including bias in AI algorithms and the potential for misuse of data.
Ownership and Authorship: The document discusses the challenge of determining ownership and authorship in AI-generated educational materials. However, the exploration of this area could be expanded to include more detailed legal and ethical considerations.
Professional Development and Training: The document suggests that educators and stakeholders need to be equipped with skills and knowledge to integrate AI into education. However, there is a gap in addressing specific strategies for professional development and training.
Considerations for Educational Developers:
Educational developers need to be vigilant about the content quality generated by AI, ensuring it aligns with educational standards and objectives. They should advocate for and participate in the creation of frameworks that address data privacy and the ethical use of AI in education. Understanding the legal nuances of AI-generated content and guiding educators on copyright issues are crucial. Additionally, there's a need to develop comprehensive professional development programs focused on integrating AI into educational practices effectively.
Part 4 - Application to Educational Development
Relevance to Educational Developers and Faculty:
Enhanced Content Creation: The document’s insights into AI's ability to generate and update educational content can inspire educational developers to explore new content creation strategies, facilitating dynamic and responsive learning environments.
Resource Curation and Personalization: AI's potential to curate and personalize learning materials can be harnessed by educational developers to create more learner-centered educational experiences, aligning resources with individual learner needs and preferences.
Collaborative Learning Facilitation: The discussion on AI's role in facilitating collaborative learning provides a foundation for educational developers to design learning activities that leverage AI for enhancing student engagement and interaction.
Prompt Engineering and Co-Creation: The concept of prompt engineering presents an innovative approach for educational developers to collaborate with AI, guiding it to produce relevant and contextually appropriate educational materials.
Adaptation and Application in Educational Development:
Educational developers can leverage AI to assist faculty in creating, updating, and personalizing course materials, encouraging a shift towards more dynamic and adaptive teaching methods. They can conduct workshops and training sessions on prompt engineering, helping faculty to effectively interact with AI tools to enhance their teaching. Additionally, educational developers can lead discussions on the ethical use of AI in education, fostering a culture of responsible AI use within institutions.
In conclusion, by understanding and addressing the gaps and challenges highlighted in the document, educational developers can play a pivotal role in shaping the integration of AI in education. They can guide educators and institutions in harnessing AI's potential responsibly and effectively, ensuring that educational practices evolve in ways that are beneficial to learners and aligned with pedagogical goals.

My Commentary

Hereto is another piece that’s thinking about the prospect of AI and OER and is also concerned about what can come from it. Bozkurt is offering a combined effort of human and AI effort to scale up something that has been growing over the past 20 years but not necessarily at the pace that we would hope, particularly when it comes to customizing and updating. Again, we’re left with questions about ownership and best ways to support faculty in this exploration. I think it offers up in one piece what many of us are thinking about in terms of possibility and problems but is still stuck (like all of us) with “well, what can we do right now that is clear and doesn’t have us backtracking?”