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Study analyzes learner-chatbot interaction patterns in AI-scaffolded writing

A new study published on arXiv analyzes learner interactions with an LLM-powered writing chatbot named Penny. Using Transition Network Analysis on over 4,500 sessions and 21,000 interactions, researchers identified two primary engagement patterns: a 'Revision Loop' for direct error correction and a 'Chat Loop' for extended dialogue. The study found that learner proficiency significantly influences these patterns, with higher-proficiency learners engaging more in negotiation and lower-proficiency learners relying on repetitive feedback cycles. AI

IMPACT Highlights the need for differentiated chatbot design to better support learners of varying proficiency levels in AI-scaffolded writing environments.

RANK_REASON The cluster contains an academic paper detailing a study on AI chatbot interactions.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Study analyzes learner-chatbot interaction patterns in AI-scaffolded writing

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Steve Woollaston, Brendan Flanagan, Yuko Toyokawa, Hiroaki Ogata ·

    Penny: Transition Network Analysis of Learner-Chatbot Interactions in Scaffolded EFL Writing

    arXiv:2607.14575v1 Announce Type: cross Abstract: Generative AI chatbots promise to transform English as a Foreign Language (EFL) writing by providing immediate, personalised feedback. However, their pedagogical value depends on how learners engage with them - a process often tre…

  2. arXiv cs.CL TIER_1 English(EN) · Hiroaki Ogata ·

    Penny: Transition Network Analysis of Learner-Chatbot Interactions in Scaffolded EFL Writing

    Generative AI chatbots promise to transform English as a Foreign Language (EFL) writing by providing immediate, personalised feedback. However, their pedagogical value depends on how learners engage with them - a process often treated as a "black box." This study uses Transition …