Name: Learning in the Wild: A Large-Scale Analysis of GenAI as a Dialogic Open Educational Resource
Start: 2026-10-08T14:15:00-0400
End: 2026-10-08T14:45:00-0400

All sessions are available online except round tables, special activities, and workshops.

Learning in the Wild: A Large-Scale Analysis of GenAI as a Dialogic Open Educational Resource

Thursday October 8, 2026 2:15pm - 2:45pm EDT

8 DR6

ID: 33533

The rapid adoption of Generative Artificial Intelligence (GenAI) has transformed how self-directed learners interact with knowledge. While GenAI tools like ChatGPT are used globally as de facto Open Educational Resources (OER), empirical evidence regarding authentic learning through these human-AI dialogues - outside of formal institutional settings - remains limited. This presentation shares the results of a large-scale, mixed-methods study that analyzes learning as it happens "in the wild."Grounded in the Dialogic OER Framework (Author, 2026), this research extends traditional OER models (like Wiley’s 5Rs) by introducing three process-oriented dimensions: Responsiveness, Reciprocity, and Reflexivity. We operationalize this framework through a computational and statistical analysis of 50,000 naturalistic conversations from the WildChat dataset - a corpus of over one million real-world ChatGPT interactions.Our methodology utilized keyword-based filtering and rule-based classification to identify 6,693 learning-oriented conversations (13.4% of the sample). These were then analyzed using natural language processing (NLP), lexical complexity metrics, and metacognitive marker detection.Key findings include:Distinct Discourse Patterns: Learning conversations exhibit significantly higher reciprocity compared to non-learning tasks, characterized by longer interaction chains (M=3.19 vs 2.41 turns) and a higher density of follow-up questions (d = 0.36, p < .001).Knowledge Co-Construction: Over 28% of learning interactions showed explicit markers of knowledge co-construction, such as critical evaluation of AI responses and iterative refinement of queries. This suggests that GenAI is not merely a static content source but a partner in emerging Open Educational Practices (OEP).The Evolution of Scaffolding: Within multi-turn learning episodes, we observed a significant increase in lexical diversity (Type-Token Ratio) alongside a decrease in verbosity (d = -0.23, p < .001). This indicates that as learners engage with the AI, their prompts become more precise and sophisticated - a sign of self-directed scaffolding and internalization.Global Equity and Access: Cross-cultural analysis revealed that regions with limited access to formal higher education, such as the Middle East and North Africa, showed the highest proportions of learning-oriented AI use (29.4%). This highlights GenAI's potential to serve as a truly open and accessible resource in underserved contexts.By presenting these findings, we aim to bridge the gap between "Openness as Content" and "Openness as Interaction." We will discuss the practical and ethical implications of these emergent technologies for the future of the open education movement, specifically how we can support learners in developing the "Reflexivity" needed to navigate AI-driven learning landscapes.

Speakers

Eyal Rabin

Lecturer, Holon Institute of Technology

Dr. Eyal Rabin is a leading researcher in artificial intelligence and education at the Institute for Applied AI Research in Education, Israel’s Ministry of Education, and a lecturer at the Holon Institute of Technology (HIT). His work focuses on the integration of artificial... Read More →

Thursday October 8, 2026 2:15pm - 2:45pm EDT
8 DR6 MIT Samberg Conference Center, 50 Memorial Drive, Cambridge MA 02139 USA

Presentation

Speaker Modality Online
Track Exploring Emergent Technologies and the Future of Openness

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OEGlobal 2026

Eyal Rabin

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OEGlobal 2026

Eyal Rabin

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