OEGlobal 2024

In response to growing demand from academics requiring resources on Artificial Intelligence (AI) for their students, Charles Sturt Librarians developed an Open Education Resource (OER) titled Using AI tools at university. This resource aims to equip university students and researchers with the knowledge and skills necessary to utilise AI tools productively, ethically and responsibly. Our project, undertaken collaboratively by Charles Sturt Librarians, seeks to democratise access to AI literacy.

Generative AI technologies and AI tools for research are increasingly prevalent in academic settings, yet students and researchers often lack guidance on the responsible and ethical use and how they can be used productively. Our OER addresses this gap by providing comprehensive information on AI tools, their applications, and ethical considerations. The resource emphasises the importance of understanding AI biases, data privacy, and the ethical implications of AI-driven decisions.

The benefits of OER for students are extensive. Research indicates that using OER enhances student learning (Cheung, 2019) and serves as an effective learning intervention by providing equal access to educational resources for all students (Grimaldi et al., 2019). Open textbooks can be continuously and easily updated to remain relevant, which is especially crucial given the rapid advancements in AI. Considering the importance of equitable access to information for our students and the challenges posed by traditional publishing models, such as high costs and restrictive licensing, OER offers valuable resources that ensure equitable access for all students.

The Pressbooks platform was used and incorporated interactive media and active learning through H5P. It seamlessly embedded in the learning management system plus allowed direct linking to specific chapters, when students had assessment requirements requiring specific AI literate information and evaluation. The project not only provided specific resources at the request of academics needing information on AI use for their students assessment tasks but was expanded to provide a complete AI literacy resource that can be used by all undertaking research. It covers algorithmic literacy (Ridley & Pawlick-Potts, 2021), understanding bias, developing competency in critical ignoring (Kozyreva et al., 2023), detecting hallucinations and communicating with AI through effective prompt engineering (Lo, 2023).

The project also had a secondary objective to familarise Librarians with developing content for an OER with then having a locally produced OER to demonstrate to academics. This initiative aligns with the broader movement towards open education and the sharing of knowledge across institutions.

Our OER, Using AI tools at university, empowers students from diverse backgrounds to engage actively with AI tools. By breaking down complex concepts into understandable modules, we foster responsible AI use and encourage student contributions to AI development. Moving forward, we aim to expand this resource and integrate it into existing digital literacy modules across disciplines. This integration will support the development of critical thinking and digital literacy skills, preparing students for the evolving digital landscape.



Included in [Session 3D]: Digital Capability, Artificial Intelligence

References
Cheung, S. K. S. (2019). A Study on the University Students’ Use of Open Educational Resources for Learning Purposes. Technology in Education: Pedagogical Innovations (pp. 146-155). Springer Singapore.

Grimaldi, P. J., Basu Mallick, D., Waters, A. E., & Baraniuk, R. G. (2019). Do open educational resources improve student learning? Implications of the access hypothesis. PloS One, 14(3), e0212508. https://doi.org/10.1371/journal.pone.0212508

Kozyreva, A., Wineburg, S., Lewandowsky, S., & Hertwig, R. (2023). Critical ignoring as a core competence for digital citizens. Current Directions in Psychological Science, 32(1), 81–88. https://doi.org/10.1177/09637214221121570

Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4), 102720. https://doi.org/10.1016/j.acalib.2023.102720

Ridley, M., & Pawlick-Potts, D. (2021). Algorithmic literacy and the role for libraries. Information Technology and Libraries (Online), 40(2), 1-15. https://doi.org/10.6017/ital.v40i2.12963

Author Keywords
Artificial Intelligence, Algorithmic Literacy, Open Educational Resources, GenAI, Digital Literacy, AI Literacy, OER, Open Textbooks

Since Open is everyone’s business, and Generative Artificial Intelligence is portrayed as a mechanism whereby to scale education for everyone everywhere, it is fundamentally problematic that large language models, which are utilised, amongst other functions, for the translation of texts, literally require a very large corpora of texts - on both sides - to function adequately. To demonstrate this, examples will be given of problematic translations from English into isiXhosa, which produce errors even at an elementary level of education.

Practitioners from the Global South realistically fear a widening of the divide as a result of the fact that many local, indigenous languages only have a small corpus of texts online. This could potentially lead to a data race, and concerns would be raised as to whether copyright may be violated in the uploading of texts. But the far more overarching concern is that of an increased dominance of already dominant languages, which could be read as a re-colonisation and negatively impact on local indigenous cultures and ways of knowing as well as impacting on the dissemination of indigenous knowledge systems.

The presentation will reflect on how Generative Artificial Intelligence functions, systematically cover issues of inclusion, diversity, equity, and access that arise as a result of using it when only a small corpus of texts is available, and then ask participants to reflect upon open education policies and strategies that arise as a result especially given potential negative impacts in relation to the Sustainable Development Goals. In particular, AI in this context not only relates to SDG 4, but also on 6 & 7 in terms of sustainability as AI consumes massive amounts of fossil fuels and also water, 9 in terms of the infrastructure required, 10 in terms of inequality and 12 in terms of responsible consumption and production.

The presentation will also refer to recent research indicating that while the power of the model has grown and grown with the size of the training datasets, that recent evidence is that these power curves are starting to level off and this has implications in terms of sustainability.



Included in [Session 3D]: Digital Capability, Artificial Intelligence

Author Keywords
Artificial intelligence, Sustainability, Open education policy and strategies, Inclusion diversity equity and access, Local Indigenous cultures and ways of knowing

The possibilities for using AI in education are exploding. AI is already widely used in education, and with the recent emergence of generative AI, the possibilities are being more actively explored. However, ethical concerns about the use of AI continue to arise. In particular, teachers, who take the lead in education, need to be empowered with ethical competencies that consider the impact of AI and digital technologies while using AI.

Accordingly, this study aims to develop a framework for teachers' ethical competencies in AI and its sub competencies and behavioral indicators. To this end, an initial competency framework and behavioral indicators were developed through a systematic literature review. At the same time, in-depth interviews were conducted with 10 in-service teachers and implications were derived according to Braun and Clarke's (2006) thematic analysis procedure.

The findings of the study, based on the synthesis of the literature review and the interview results, revealed a set of AI ethics competencies for teachers consisting of awareness, judgment, and practice, with corresponding sub-competencies and behavioral indicators. This study has significance in that it systematically presents the ethical competencies of teachers for coexistence with AI amid the ongoing development of AI from a post-humanistic perspective.



Included in [Session 3D]: Digital Capability, Artificial Intelligence

Author Keywords
AI in Education, Ethical Competence, Teacher Education

Fostering faculty participation in adopting Open Educational Resources can be challenging when faculty lack full competency in digital literacy. Digital literacy is a pillar of UNESCO's Sustainable Development Goals and integral to the adoption of OER. Creating resources and training to provide faculty comfort in learning digital literacy can help in the buy-in and adoption of OER. This requires collaboration in a variety of areas across campus and identification of people who can teach these skills in multiple areas.

This presentation will focus on how to use public relations tactics to create buy-in among faculty members that promotes institutional spread of digital literacy and OER across a small, non-profit minority-serving institution in the United States.



Included in [Session 3D]: Digital Capability, Artificial Intelligence

References
https://prsa.org iabc.com

Author Keywords
Digital competence, Public Relations, Open Educational Practices, Open Educational Strategies

This study explores the integration of Generative Artificial Intelligence (GenAI) into robotics programming education to enrich inquiry-based science learning, particularly in the STEAM (Science, Technology, Engineering, Arts, and Mathematics) domains, with a focus on its impact on elementary science education. Through hands-on STEAM activities, students enhance problem-solving skills, collaboration, and develop a strong interest in science learning. Utilizing Scratch, a free and open programming language, students not only learn programming basics but also deepen their understanding and application of scientific concepts. The research targets elementary school students, incorporating technology, mathematics, and physical education into a series of STEAM education experiments. Results underscore the importance of open educational resources in supporting STEAM education, enhancing scientific learning, fostering creativity, and teamwork, thereby positively influencing educational equity and quality. The example of a baseball robot illustrates the potential benefits and challenges of utilizing open educational resources.

Inquiry-based science learning encourages questioning, investigation, and knowledge construction through exploration and experimentation. Recent advancements in Artificial Intelligence, especially Generative AI (GenAI), offer novel tools to enhance this educational approach. This paper examines how integrating GenAI can enrich the learning experience, focusing on a STEAM project involving the design and implementation of a baseball robot.

Utilizing the 6E experiential learning model, GenAI assumes multiple roles across the learning stages. Initially, in the Engage phase, GenAI acts as a catalyst, captivating student interest through Scratch, thereby igniting curiosity. In the subsequent Explore phase, GenAI transitions into a mentor, providing tailored learning pathways and resources, facilitating guided exploration. As the learning progresses into the Explain phase, GenAI transforms into an instructor, simplifying intricate concepts and theories through textual content. During the Engineer phase, GenAI serves as a design assistant, assisting students in utilizing tools like LEGO SPIKE for project development. Moving forward to the Enrich phase, GenAI becomes an inspiration, expanding students' knowledge and fostering interdisciplinary integration and innovative thinking. Finally, in the Evaluate phase, GenAI transitions into an assessor, delivering real-time feedback and assessments to aid students and teachers in reviewing and reflecting on learning outcomes. GenAI plays a crucial role in scientific inquiry activities, offering expertise, guidance, and support throughout the project phases, thereby enriching students' learning experiences and fostering knowledge exchange in STEAM fields.

The combination of GenAI and Open Educational Resources (OER) in STEAM education enhances learning by personalizing pathways, improving accessibility, and ensuring quality education for all. This model fosters students' passion for science and technology, enhances problem-solving skills, and cultivates future innovators. It demonstrates the potential of Generative AI in modern education, emphasizing the importance of open education in global learning initiatives.



Included in [Session 3D]: Digital Capability, Artificial Intelligence

References
Burke, D. (2014). E byDeSGN" Model. Chiou, G.-L., Lee, M.-H., & Tsai, C.-C. (2013). High school students’ approaches to learning physics with relationship to epistemic views on physics and conceptions of learning physics. Research in Science & Technological Education, 31(1), 1-15. https://doi.org/10.1080/02635143.2013.794134

García-Carmona, A. (2020). From Inquiry-Based Science Education to the Approach Based on Scientific Practices. Science & Education, 29(2), 443-463. https://doi.org/10.1007/s11191-020-00108-8

Hwang, G.-J., Yang, L.-H., & Wang, S.-Y. (2013). A concept map-embedded educational computer game for improving students' learning performance in natural science courses. Computers & Education, 69, 121-130. https://doi.org/10.1016/j.compedu.2013.07.008

Inguva, P., Shah, P., Shah, U., & Brechtelsbauer, C. (2021). How to Design Experiential Learning Resources for Independent Learning. Journal of Chemical Education, 98(4), 1182-1192. https://doi.org/10.1021/acs.jchemed.0c00990

Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. FT press.

Kuen-Yi Lin, H.-S. H., P. John Williams & Yu-Han Chen. (2020). Effects of 6E-oriented STEM practical activities in cultivating middle school. https://doi.org/10.1080/02635143.2018.1561432

Li, X., Muniz, M., Chun, K., Tai, J., Guerra, F., & York, D. M. (2022). Inquiry-Based Activities and Games That Engage Students in Learning Atomic Orbitals. J Chem Educ, 99(5), 2175-2181. https://doi.org/10.1021/acs.jchemed.1c01023

Pintrich, P. R., & De Groot, E. V. (1990). Motivational_and_self_regulated_learning. https://doi.org/10.1037/0022-0663.82.1.33

States, N. L. (2013). Next Generation Science Standards: For States, By States. The National Academies Press. https://doi.org/doi:10.17226/18290

Wang, H.-H., Moore, T. J., & Roehrig, G. H. (2011). STEM Integration: Teacher Perceptions and Practice. Journal of Pre-College Engineering Education Research. https://doi.org/10.5703/1288284314636

Author Keywords
STEAM, Programming Education, Exploratory Learning, Generative Artificial Intelligence, Computational Thinking

Title and talk TBA but we might expect from recent talks Martin will be speaking about the future of open education and artificial intelligence. It will be interesting!

Included in [Session 11A]: Artificial Intelligence

AI pervades various facets of society, including education. In the open education domain, the notion of open AI, distinct from the entity OpenAI, is attracting attention. However, the precise connotations of "open" in conjunction with "AI" remain subject to diverse interpretations, reflecting conceptual tensions and longstanding differences around concepts of “open” in respective domains.

In a blog post last year, prominent edtech expert David Wiley wrote, “when people talk about whether or not generative AI should be “open,” they could be talking about whether the foundation models should be open, whether the modified model weights that result from fine-tuning should be open, and/or whether the prompts (which includes templates, embeddings, etc.) should be open” (Wiley, 2023). In a similar direction, focusing on licensing for different aspects of AI technology, the Open Source Initiative are currently developing an “open-source AI” definition (Open Source Initiative, 2024). On the other hand, in broader literature, definitions of open education and open technology encompass a wide range of concepts, where “open” could mean: “ethical” (Holmes et al., 2022), “inclusive” and “innovative” (Bozkurt, 2023), “co-created” and “learner-driven” (Walberg and Thomas, 1972), “non-proprietary” (Berners-Lee, 2023), “decentralised” (Crowston and Howison, 2005), “accessible without barriers” (Knox, 2013), “available to join”, “shared”, “not tightly controlled” (Weller, 2020), “available in the public domain or under an open license” (UNESCO, 2022), “interpretable” and “visible” (Conati, Porayska-Pomsta, and Mavrikis, 2018), and many others.

This indicates that “open AI” and “open-source AI” are overlapping but not identical concepts. “Open-source AI” seems to be about the tangible aspects of systems, whereas “open AI” is broader and potentially includes context of how systems can be used, who can use them, and what for. Rather than trying to resolve these debates into a single taxonomy/typology, we propose a meronomic, holistic account of openness in AI education which explores the relationship between definitions with respect to part-whole relationships. This will facilitate diverse contributions and critical discussion.

In this panel session, participants will have the opportunity to engage in the panel discussion and ask questions regarding the dimensions of openness of AI in education.

The agenda includes:

• 10 minutes: introductions, pre-recorded 1-slide lightning presentations from experts, panel reactions
• 10 minutes: panel interaction and debate
• 15 minutes: Q&A with panel members based on thoughts submitted by the audience (with backup questions prepared by the moderators).
• 5 minutes: closing discussion and synthesis.

Experts who agreed to contribute so far include David Wiley, Anne-Marie Scott, Aras Bozkurt, Chrissi Nerantzi and Leo Havemann. Some of them might join virtually. They will be provided with prompts ahead of time for their initial statements. Robert Farrow, an experienced moderator, will chair the panel discussion. During the discussion, delegates will be able to contribute reflections and questions through a back channel and these will be integrated into the discussion.



Included in [Session 11A]: Artificial Intelligence

References
Berners-Lee, T. (2023). Frequently asked questions by the Press – Tim BL (w3.org). Available at https://www.w3.org/People/Berners-Lee/FAQ.html (Accessed: 2 May 2024) 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), pp. 178–184. DOI: https://doi.org/10.55982/openpraxis.15.3.579 Conati, C., Porayska-Pomsta, K. and Mavrikis, M., 2018. AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling. arXiv preprint: https://arxiv.org/abs/1807.00154 Crowston, K. and Howison, J. (2005). The social structure of Free and Open Source software development. First Monday, Volume 10, Number 2 - 7 February 2005 Available at: https://firstmonday.org/ojs/index.php/fm/article/download/1207/1127 (Accessed: 2 May 2024) Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S.B., Santos, O.C., Rodrigo, M.T., Cukurova, M., Bittencourt, I.I. and Koedinger, K.R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, pp.1-23. DOI: https://doi.org/10.1007/s40593-021-00239-1 Knox, J. (2013). Five critiques of the open educational resources movement. Teaching in Higher Education, 18(8), pp. 821–832. DOI: https://doi.org/10.1080/13562517.2013.774354 Open Source Initiative (2024). The Open Source AI Definition – draft v. 0.0.8. Available at: https://opensource.org/deepdive/drafts/the-open-source-ai-definition-draft-v-0-0-8 (Accessed: 2 May 2024) UNESCO (2022). Understanding Open Science. UNESDOC Digital Library. DOI: https://doi.org/10.54677/UTCD9302 Walberg, H.J. and Thomas, S.C. (1972). Open education: An operational definition and validation in Great Britain and United States. American Educational Research Journal, 9(2), pp.197-208. DOI: https://doi.org/10.3102/00028312009002197 Weller, M. (2020). 25 years of ed tech. Athabasca University Press. DOI: https://doi.org/10.15215/aupress/9781771993050.01 Wiley, D. (2023). An analogy for understanding what it means for generative AI to be “Open”. Open Content. https://opencontent.org/blog/archives/7289

Author Keywords
Open AI, Open-source AI, Artificial Intelligence, Open data, Open-source technical platforms

The burgeoning integration of Large Language Models (LLMs) such as ChatGPT into the fabric of Massive Open Online Courses (MOOCs) has highlighted a promising new direction for enhancing automated essay assessment processes. This research delves into the practical implementation of LLMs for evaluating student essays within MOOC frameworks, focusing primarily on exploring advanced prompt engineering strategies.

We investigate a spectrum of methodologies, including few-shot learning, Chain-of-Thought (CoT) prompting, and fine-tuning techniques, to discern the most effective strategies for harnessing the capabilities of LLMs in this educational domain. Drawing from the latest advancements in natural language processing (NLP), our study examines the ability of LLMs to deliver accurate, efficient, and scalable assessments of student submissions.

MOOCs typically host hundreds to thousands of students per course, presenting significant logistical challenges regarding assignment evaluation. The volume of essays that require assessment can be overwhelming for instructors, making it virtually impossible to provide detailed, timely feedback without technological assistance. The deployment of LLMs promises not only to enhance the grading efficiency and maintain consistency in evaluation standards across large cohorts.

The primary objective of this study is to explore the application of generative AI (GAI) in assisting with essay grading, utilizing open courses hosted at ewant, the largest MOOCs platform run by National Yang Ming Chiao Tung University (NYCU) in Taiwan . This course, "Required Credits for University Students - Emotional Education" is taught by Professor Chen Fei-Chuan at National Yunlin University of Science and Technology, Taiwan. Since its first delivery in 2015, this course has been offered 137 times, with nearly 20,000 students enrolled. From both qualitative and quantitative perspectives, this course represents an optimal choice for the study, offering substantial potential for further research and development. Assignments in this course predominantly involve open-ended questions without standard answers, encouraging students to reflect, discuss, share, and synthesize their personal experiences based on the knowledge acquired during the course. This type of unstructured assignment is better suited for introducing GAI than structured assignments in science and engineering courses with definitive answers.

This research aims to leverage a data-driven approach to develop a GAI system that replicates the grading standards and performance of the instructors or teaching assistants (graders), thereby assisting future educators in efficiently grading large volumes of written assignments. By analyzing the strengths and drawbacks of multiple prompt engineering and fine-tuning methods in automating essay evaluations, the study aims to establish a dataflow pipeline for AI-assisted essay grading, with the expectation of generalizing this process to other courses of a similar nature. Additionally, this research proposes recommendations for designing more effective and scalable automated essay assessment systems tailored for contemporary online education platforms.

Overall, this study aims to provide a comprehensive analysis of the potential of LLMs in transforming the landscape of essay assessment in MOOCs, thereby contributing valuable insights into the optimization of educational technologies in a GAI era.



Included in [Session 11A]: Artificial Intelligence

References
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in neural information processing systems, 35, 22199-22213.

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022). Rethinking the role of demonstrations: What makes in-context learning work?. arXiv preprint arXiv:2202.12837.

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., ... & Le, Q. V. (2021). Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837.

Author Keywords
Artificial Intelligence, Large Language Models, Prompt Engineering, Assessment, MOOCs