Students\u2019 rapid uptake of Generative Artificial Intelligence tools, particularly large language models (LLMs), raises urgent questions about their effects on learning. We compared the impact of LLM use to that of traditional note-taking, or a combination of both, on secondary school students\u2019 reading comprehension and retention. We conducted a pre-registered, randomised controlled experiment with within- and between-participant design elements in schools in England. 405 students, aged 14-15 years, studied two text passages and completed comprehension and retention tests three days later. Quantitative results demonstrated that both note-taking alone and combined with LLM use had significant positive effects on retention and comprehension compared to using the LLM alone. Yet, most students preferred using the LLM over note-taking, and perceived it as more helpful. Qualitative results revealed that many students valued the LLM for making complex material more accessible and reducing cognitive load, while they appreciated note-taking for promoting deeper engagement and aiding memory. Additionally, we identified “archetypes” of prompting behaviour, offering insights into the different ways students interacted with the LLM. Overall, our findings suggest that, while note-taking promotes cognitive engagement and long-term comprehension and retention, LLMs may facilitate initial understanding and student interest. The study reveals the continued importance of traditional learning activities, the benefits of combining LLM use with traditional learning over using LLMs alone, and the AI skills that students need to maximise those benefits.<\/p>\n","protected":false},"excerpt":{"rendered":"
Students\u2019 rapid uptake of Generative Artificial Intelligence tools, particularly large language models (LLMs), raises urgent questions about their effects on learning. We compared the impact of LLM use to that of traditional note-taking, or a combination of both, on secondary school students\u2019 reading comprehension and retention. We conducted a pre-registered, randomised controlled experiment with within- […]<\/p>\n","protected":false},"featured_media":1156613,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Computers & Education","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Pia Kreijkes, Viktor Kewenig, Martina Kuvalja, Mina Lee, Jake M. Hofman, Sylvia Vitello, Abigail Sellen, Sean Rintel, Daniel G. Goldstein, David Rothschild, Lev Tankelevitch, and Tim Oates. 2025. 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