English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Personalized chatbot-based teaching assistants can be crucial in addressingincreasing classroom sizes, especially where direct teacher presence islimited. Large language models (LLMs) offer a promising avenue, with increasingresearch exploring their educational utility. However, the challenge lies notonly in establishing the efficacy of LLMs but also in discerning the nuances ofinteraction between learners and these models, which impact learners'engagement and results. We conducted a formative study in an undergraduatecomputer science classroom (N=145) and a controlled experiment on Prolific(N=356) to explore the impact of four pedagogically informed guidancestrategies on the learners' performance, confidence and trust in LLMs. DirectLLM answers marginally improved performance, while refining student solutionsfostered trust. Structured guidance reduced random queries as well as instancesof students copy-pasting assignment questions to the LLM. Our work highlightsthe role that teachers can play in shaping LLM-supported learning environments.

Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception