Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning

Synthesis: Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning

Key Findings

In a large-scale survey of 776 German participants conducted in February 2025 — the third year following the breakthrough launch of ChatGPT — 87% of respondents reported already incorporating LLMs into their everyday learning routines for a wide variety of learning tasks (11% used LLMs but not for learning, and 2% did not use them at all).

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: LLM learners averaged 31 years versus 35 for non-learners (F = 6.68, p < .01), men were more likely to adopt (53% of LLM learners were male, while 64% of avoiders were female; χ² = 15.5, p < .001), and ChatGPT dominated, with 93% (n = 632) of LLM learners reporting its use.

Four types of learners emerge across learning contexts, distinguished by the tasks they perform with LLMs and the devices they use to access them: Structured Knowledge Builders (22.9%), Self-Guided Explorers (26.5%), Analytical Problem Solvers, and Adaptive Power Users, identified via latent class analysis with balanced class shares (23.8–25.7%).

Respondents exhibit paradoxical trust behaviors: they rely on LLMs while simultaneously distrusting their accuracy and privacy protection measures — 88% (n = 601) perceived improved learning or productivity, yet misinformation was the most commonly cited challenge (68%, n = 460) and the majority (n = 543) reported taking no privacy measures.

The study's 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 — "learning by design."

Study Design & Method

The study addresses who is embracing LLMs for self-directed learning, who remains hesitant, their reasons for adoption or avoidance, and the learning patterns that emerge with this novel technological landscape. Data come from an online survey administered via Prolific in February 2025 to 776 German participants (mean age 31.6, SD = 9.87, max 72), including non-adopters; 678 respondents were actively using LLMs for learning. Analysis combined descriptive and inferential statistics (ANOVA, chi-square), a latent class analysis from which the four-learner typology was derived, multinomial logistic regression predicting class membership from privacy perceptions, effectiveness, over-reliance, and demographics, and qualitative analysis of open-text responses. Adoption drivers included curiosity (n = 517), recommendations from social connections (n = 254), and media coverage (n = 217), while avoidance was led by mistrust of factual output and a preference for traditional methods (52% of non-LLM learners).

Implications for AI in Education

The near-universal reported use of LLMs in informal, everyday learning indicates that much AI-mediated learning now happens outside formal curricula, with implications for Lifelong Learning and for how formal institutions relate to self-directed learners. The paradoxical trust findings — reliance alongside distrust of accuracy and privacy — suggest that learners need support in calibrating their use, pointing to the role of source provision and transparency features in tool design. The four learner types argue against one-size-fits-all interfaces and content, favoring designs that accommodate varied task-device combinations; the call for multi-modal and collaborative features connects to Collaborative Learning and to broader questions of Motivation and engagement in self-regulated, informal contexts. Use is embedded and flexible — the median learner spent about 20 minutes per weekday on LLM-based learning, nearly 70% reported no particular day of the week, and 63.5% had used LLMs for more than six months — and 58% (n = 395) said they were extremely likely to continue using LLMs for learning, with 72% willing to recommend them to friends, reinforcing that Self Regulated Learning with LLMs is now a durable everyday practice rather than a novelty.

Limitations

The authors note that the sample, recruited exclusively from Germany via Prolific, may overrepresent tech-savvy, English-fluent individuals (mean AI literacy score = 3.06), and that cultural attitudes toward AI vary, so findings may not generalize to non-Western or less tech-savvy populations. The non-user subsample was small, so results for that group should be treated as exploratory and descriptive. The study measured self-reported LLM use and perceived purposes without measuring concrete learning outcomes, so no claims can be made about whether more frequent or highly satisfactory LLM use translates into deeper conceptual learning.

Connected Concepts

Lifelong Learning

Self Regulated Learning

Motivation

Collaborative Learning

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Citation

Terzimehić, N., Bühler, B., & Kasneci, E. (2026). Conversational AI as a catalyst for informal learning: An empirical large-scale study on LLM use in everyday learning.