Large language models can support learning and teaching across education levels, from reading and writing support for elementary students to professional training¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The authors argue LLMs can assist learners at all levels, e.g., elementary students' reading and writing skills, via generated questions, prompts, summaries and explanations. → Kasneci 2023

Evidence¶

Kasneci 2023¶

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. https://doi.org/10.1016/j.lindif.2023.102274

q1 · i? · theoretical · r2

Position-paper argument (type e) in Section 1.1 on opportunities for learning: the authors assert, without reporting new data, that LLMs can assist "the development of reading and writing skills" and critical thinking for elementary students, among uses across primary to professional levels.

"For elementary school students, large language models can assist in the development of reading and writing skills (e.g., by suggesting syntactic and grammatical corrections), as well as in the development of writing style and critical thinking skills."

Discussion¶

Related Claims¶

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• LLMs act as catalysts for collaboration by aiding idea generation, organization and peer feedback, and by democratizing access to knowledge in large-class settings — a narrower finding that bears on this claim
• Incorporating example problems does not necessarily improve LLM teaching and can degrade it via a shift to example-specific error correction — related
• Phase VI AI-enabled systems face six documented challenges: explainability, hallucination risk, prompt sensitivity, computational cost, validation complexity, and limited large-scale evidence — related
• LLM-simulated student ideas moderately overlap with teacher- and student-generated lesson ideas (mean cosine similarity 0.52), with larger LLMs slightly outperforming Mistral-7B — related
• Student-centered writing environments are argued to lower anxiety, increase confidence, and provide natural language contexts — related
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