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

arXiv: https://arxiv.org/abs/2607.14575 · HTML: https://mlsanity.com/paper/2607.14575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 34 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 1 of 1)

- [Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes](https://mlsanity.com/paper/2411.15486/) (2024)

## Built on by (most cited first, 1 of 1)

- [Accurate but Natural? Diagnosing Grammatical and Idiomatic Gaps in Japanese EFL Writing](https://mlsanity.com/paper/2608.09289/) (2026)

## Abstract (arXiv)

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 Network Analysis to model the temporal dynamics of Japanese EFL learners using "Penny," an LLM-powered writing chatbot. Analysis of over 4,500 writing sessions and 21,000 chatbot interactions reveals two dominant behavioural loops: a "Revision Loop," where feedback leads directly to successful error correction, and a "Chat Loop," where learners engage in sustained dialogue with the chatbot following feedback. Crucially, EFL proficiency significantly shapes interaction: high-proficiency learners engage more in open dialogue and negotiation with the chatbot, while low-proficiency learners rely more heavily on repetitive corrective feedback cycles. The findings demonstrate that AI-scaffolded writing is a non-linear, dialogic process and highlight the need for differentiated chatbot design to move beyond simple error correction and foster deeper cognitive engagement for all learners.