Agentic education · Learning science

Actionable companion · Retention & transfer

Learning with agency: AI-driven psychological and cognitive decision processes in professional practice: a systematic review using music teachers as an instrumental case

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.

Read original paperSearch paper agentCC BY 4.0

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Abstract Artificial intelligence (AI) is increasingly entering professional practice, raising questions about how professionals interpret algorithmic authority, protect judgment autonomy, and negotiate human–AI boundaries. This systematic review uses music teachers as an instrumental case to examine AI-related psychological responses, cognitive appraisals, and professional decisions in a judgment-intensive and emotionally involved educational context. Following PRISMA 2020, 20 studies published from 2023 onwards were synthesized through thematic synthesis, directed content analysis, and higher-order evidence-to-theme mapping. MMAT 2018 was used for quality appraisal, with evidence-tiering applied to classify evidence strength. The synthesis identified four interrelated psychological–cognitive–professional decision pathways: professional boundaries and retention of judgment authority, learning-process regulation and critical engagement, adaptive co-creation and professional capacity reconstruction, and risk perception with bounded adoption. Across these pathways, music teachers did not simply accept or reject AI; rather, they selectively integrated AI by weighing technological convenience, pedagogical value, professional responsibility, and risks to student agency or cultural interpretation. A further synthesis showed that the depth and mode of AI adoption were jointly shaped by individual capability, organizational environment, and technological characteristics. The review suggests that AI training for music teachers should move beyond tool operation toward judgment-oriented preparation, including task-suitability evaluation, output review, critical-use pedagogy, and ethical guidance. For psychology-related professions, the findings should be treated as hypothesis-generating propositions rather than direct clinical recommendations, because psychological service contexts involve higher ethical responsibility, decision-making risk, and professional accountability. Keywords: AI adoption, artificial intelligence, cognitive appraisal, human–AI collaboration, music teachers, professional identity, professional judgment, psychological mechanisms

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Age / stage not established. 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

This study was conducted as a systematic literature review and organized according to the PRISMA 2020 reporting framework, including study identification, screening, eligibility assessment, and inclusion. To enhance transparency and reproducibility, each stage of the selection process is described step by step in the text, while Figure 1 reports the number of records retrieved from each database, records removed before screening, records screened by title and abstract, reports assessed for full-text eligibility, and studies finally included in the review.

Limitations and uncertainty · source passage

First, this review focused on formally published English-language studies from 2023 onwards. This decision helped capture the new phase in which AI entered educational practice after ChatGPT, but it may also have excluded earlier digital music education studies, non-English contexts, and unpublished practice-based reports. As a result, the cultural and institutional diversity of music teachers’ AI-related experiences may not have been fully represented.

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.

  • Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.
  • 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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Yuchen Wang; Yanqing Lan. AI-driven psychological and cognitive decision processes in professional practice: a systematic review using music teachers as an instrumental case. Frontiers in Psychology (2026-06-15). https://doi.org/10.3389/fpsyg.2026.1866711

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.