Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning
Not on the map. Why?
Abstract (arXiv)
Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as diverse user groups explore these novel tools. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We present an in-depth analysis from a large-scale survey of 776 participants, showcasing that 88% of our respondents already incorporate LLMs into their everyday learning routines for a wide variety of (learning) tasks. Young adults are at the forefront of adopting LLMs, primarily to enhance their learning experiences independently of time and space. Four types of learners emerge across learning contexts, depending on the tasks they perform with LLMs and the devices they use to access them. Interestingly, our respondents exhibit paradoxical behaviours regarding their trust in LLMs' accuracy and privacy protection measures. Our implications emphasize the importance of including different media types for learning, enabling collaborative learning, providing sources and meeting the needs of different types of learners and learning by design.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Jun 2025.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2026Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Modelscited by 5
- 2025Beyond "Hallucinations": A Framework for Stable Human-AI Reasoningcited by 3
- 2026Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologismscited by 1
- 2026Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactionscited by 0
- 2026Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarkscited by 0
- 2026Sycophancy is an Educational Safety Risk: Why LLM Tutors Need Sycophancy Benchmarks14 May 2026
- 2026Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms26 Apr 2026
- 2026Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions24 Feb 2026
- 2026Where Knowledge Collides: A Mechanistic Study of Intra-Memory Knowledge Conflict in Language Models14 Jan 2026
- 2025Beyond "Hallucinations": A Framework for Stable Human-AI Reasoning16 Oct 2025