Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: Beyond Linear Growth: Adaptive Human Intelligence, Emotional Dynamics, and Willingness to Write in Artificial Intelligence-Mediated Informal Digital Learning of English (AI-IDLE)

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract The development of generative artificial intelligence (GenAI) is expanding AI-mediated informal digital learning of English (AI-IDLE) from predominantly receptive and conversational activities to cognitively demanding productive tasks such as academic writing. Yet little is known about how learners’ motivation and technology-related emotions develop over time, or how sustained interaction with GenAI may engage human capacities for evaluation and adaptation. Grounded in Complex Dynamic Systems Theory (CDST), this study investigated the longitudinal development of willingness to write (WTW), technology-related enjoyment (ENY), and technology-related anxiety (ANX) among Chinese undergraduates learning English as a Foreign Language (EFL) in teacher-supported AI-IDLE embedded within an 11-week English for Academic Purposes writing course. Twenty-six participants with complete records across 11 measurement occasions were included in the longitudinal analysis. Trajectory analysis, change-point analysis, and exploratory moving-window correlation analysis were combined with thematic analysis of learners’ reflections. WTW followed a nonlinear trajectory, declining during the middle period before a potential late-stage transition and subsequent increase, whereas ENY remained comparatively stable and ANX generally declined. The relationships of WTW with ENY and ANX also varied over time. Learners’ reflections revealed increasing recognition of human–AI complementarity and more selective, critical, and strategic engagement with GenAI. From a human-intelligence perspective, these patterns illustrate adaptive and metacognitive capacities involved in evaluating AI-generated information, adjusting strategies, and coordinating human and artificial resources. Rather than treating WTW or emotions as measures of intelligence, the findings position AI-IDLE as a context in which such human intellectual capacities may be exercised and expressed. Keywords: human intelligence, willingness to write, Complex Dynamic Systems Theory, technology-related enjoyment, technology-related anxiety, AI-IDLE

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The study was conducted in an EAP writing course involving 127 Chinese senior undergraduate students from Humanities and Social Sciences (HSS) disciplines. Participants were recruited from students enrolled in this course, and participation in the research was entirely voluntary. At the beginning of the course, the instructor introduced a GenAI chatbot as an optional learning resource and demonstrated how it could support idea generation, drafting, revision, language refinement, and source exploration for academic writing. Rather than integrating the chatbot into compulsory classroom activities, the instructor recommended its use for completing weekly out-of-class writing tasks while leaving the decision of whether, when, and how to use the chatbot entirely to individual students. This learning arrangement reflected the principles of AI-IDLE, in which learners voluntarily engage with AI technologies beyond formal instructional requirements to support their own language learning.

Limitations and uncertainty · source passage

During T3–T6, reflections increasingly addressed more demanding aspects of academic writing, including organising arguments, identifying research gaps, and developing introductions and literature reviews. Students described learning how to connect sections and explain the rationale for their research. Twenty-three students raised concerns about the depth or specificity of the chatbot’s guidance, and 23 identified limitations in personalisation or task fit. These overlapping themes concerned whether general frameworks could provide sufficiently detailed guidance for particular research topics.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Chenghao Wang; Lanfang Sun; Bin Zou; Yunyan Zhang. Beyond Linear Growth: Adaptive Human Intelligence, Emotional Dynamics, and Willingness to Write in Artificial Intelligence-Mediated Informal Digital Learning of English (AI-IDLE). Journal of Intelligence (2026-10-13). https://doi.org/10.3390/jintelligence14100259

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.