Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Threat-related AI anxiety and engagement in live-streamed AI courses: a moderated serial mediation model of extrinsic motivation and teacher support

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): Adult & professional learning. Verify the participant population and task before transferring the findings.

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

Background As artificial intelligence becomes increasingly embedded in education and work, live-streamed AI courses have become a common format for timely, interactive, and practice-oriented instruction. At the same time, AI-related uncertainty has intensified learners’ anxiety. This study examined how threat-related AI anxiety is associated with engagement in live-streamed AI courses through extrinsic motivation for AI learning and perceived continuous teacher support, and whether this process varies by AI learning self-efficacy. Methods A questionnaire survey was conducted among 487 adult learners in China who had participated in at least one live-streamed or synchronous AI-related course within the previous 6 months. Data were analyzed using PROCESS Model 87 with 5,000 bootstrap samples. Results Threat-related AI anxiety was positively associated with extrinsic motivation for AI learning. Extrinsic motivation was positively associated with both perceived continuous teacher support and engagement, and perceived continuous teacher support was positively associated with engagement. The serial indirect effect through extrinsic motivation and perceived continuous teacher support was significant. AI learning self-efficacy significantly weakened the association between perceived continuous teacher support and engagement, such that learners with lower self-efficacy depended more heavily on teacher support for their engagement. Conclusion Engagement in live-streamed AI courses is associated with a linked process involving emotional pressure, motivational activation, and instructional support. The study extends research on AI anxiety from motivation to engagement, identifies continuous teacher support as a key mechanism in live-streamed AI learning, and highlights the importance of accessible and supportive course design, especially for learners with lower AI learning self-efficacy.

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Adult & professional learning. 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 present study employed a cross-sectional, questionnaire-based survey design. The research context was commercial social media platforms in China, which have become a major channel for synchronous online AI education and skill development. These platforms host a large number of educational providers offering short-video-based promotional content and live-streamed AI courses, making them a highly relevant setting for examining learners’ engagement in live-streamed AI courses.

Limitations and uncertainty · source passage

The remainder of this paper is structured as follows. Section 2 reviews the existing literature and formulates the research hypotheses. Section 3 outlines the research methodology, detailing the participant sample, data collection procedures, and measurement instruments. Section 4 presents the results of the statistical analyses and hypothesis testing. Section 5 discusses the theoretical contributions and practical implications of the findings, while also acknowledging the study’s limitations and suggesting directions for future research. Finally, Section 6 provides a brief conclusion.

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.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

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. Use a bounded job-relevant simulation or professional-development task with an expert supervisor. Require a traceable rationale and verification before any real-world action. 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. Assess independent performance on an unfamiliar simulation, retention after a delay, error severity and total review time. Do not equate a faster AI-assisted response with durable professional competence. 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

Ming Lei; Yue Zou; Xin Wang. Threat-related AI anxiety and engagement in live-streamed AI courses: a moderated serial mediation model of extrinsic motivation and teacher support. Frontiers in Psychology (2026-04-29). https://doi.org/10.3389/fpsyg.2026.1831785

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.