learner-behavior
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- PaperComputers and Education: Artificial Intelligence22 Jun 2026
Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning
Nađa Terzimehić, Babette Bühler, Enkelejda Kasneci
A large-scale survey of 776 German participants found that 88% already incorporate LLMs into their everyday learning routines, with young adults leading adoption. Four distinct learner types emerged based on tasks and devices, though users showed paradoxical trust in LLM accuracy and privacy. The findings highlight the need for diverse media, collaborative learning, and design that supports different learner needs.
Original abstract
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 these novel tools become easily and widely available. 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 German participants, showcasing that 88% of our respondents already incorporate LLMs into their everyday learning routines for a wide variety of (learning) tasks. Young adults among German-based, digitally engaged users 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.
- PaperComputer Assisted Language Learning8 May 2026
More is less? Depth of vocabulary search on smartphones among Chinese learners of Japanese as a foreign language
Yuzhe Li
Investigates the relationship between the depth of vocabulary search on smartphones and vocabulary acquisition among Chinese learners of Japanese, suggesting that more extensive searching may not lead to better retention.
- PaperComputer Assisted Language Learning7 May 2026
Comparative analysis of high and low performers’ use of generative AI in EFL academic writing: behavioral patterns and perceptions
Yao Lu, Chengyuan Jia
This study compares how high and low performing EFL students use generative AI tools in academic writing, examining their behavioral patterns and perceptions.
- PaperComputer Assisted Language Learning5 May 2026
Learning analytics on multimodal GAI-driven EFL oral learning: uncovering learning behavior clusters with motivation and performance dynamics
Yuting Chen, Morris Siu-Yung Jong, Michael Yi-Chao Jiang, Ming Li
This study uses learning analytics to analyze multimodal data from generative AI-driven EFL oral learning, identifying clusters of learning behaviors and examining their relationships with motivation and performance dynamics.