Actionable companion · Feedback & scaffolding
From AI evidence to responsible practice: Exploring large language model applications in dance education from an educational psychology perspective
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
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 participants, intervention and comparator before applying this study in a new setting.
Original authors’ abstract, reproduced under the linked source license:
Introduction This study proposes a psychology informed framework for exploring the application of large language models (LLMs) in dance education. To address the limited personalization of traditional dance instruction and its insufficient adaption to learners' cognitive and emotional characteristics, a conceptual methodology is introduced with three components: Preliminaries, the DanceGPT, and Adaptive Knowledge Integration Strategy (AKIS). Methods Within this framework, DanceGPT is designed as a multimodal architecture that integrates visual, motion, and textual information through multimodal encoding, learner aware personalization, Dance-Aware Attention, and an Adaptive Knowledge Integration Strategy (AKIS). From a system perspective, the current implemented and evaluated core is a multimodal video based action quality assessment model, in which computer vision and pose derived motion signals are fused to predict expert referenced performance scores, while the LLM-oriented tutoring and feedback functions remain part of the broader pedagogical framework. Experiments are conducted on the AQA-7 and FineDiving benchmarks under a fully supervised offline action quality regression setting. Results and discussion Results show that the proposed model achieves consistent improvements over competitive baselines in ranking correlation and regression accuracy, demonstrating the effectiveness of multimodal fusion, domain guided attention, learner aware conditioning, and adaptive knowledge integration for dance related performance evaluation. Educational psychology is incorporated in this study primarily as a theoretical design lens for personalization and adaptive feedback rather than as a directly validated outcome dimension. The present work does not directly measure learning outcomes, tutoring effectiveness, or long term cognitive and emotional development. The reliance on multimodal modeling introduces non-trivial computational cost. These limitations indicate that future research should further validate the framework through real learner studies, interactive tutoring scenarios, and longitudinal evaluations in authentic dance education settings.
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
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 section provides a detailed examination of the integration of large language models (LLMs) into dance education, framed within the context of educational psychology. The primary objective of this study is to explore the potential of LLMs to enhance learning processes, refine pedagogical methodologies, and address domain-specific challenges inherent to dance education. To systematically approach this objective, the methodology is structured into three core components: Preliminaries, Cognitive-Informed Generative Framework, and Adaptive Pedagogical Strategy.
Limitations and uncertainty · source passage
Dance education serves as a cornerstone for cultural and artistic development, fostering creativity, emotional expression, and physical coordination. Despite its significance, traditional methods in dance education often struggle to meet the diverse needs of learners, provide personalized feedback, and incorporate psychological principles to optimize learning outcomes (Andersson, 2014). The integration of artificial intelligence (AI) into this domain offers transformative possibilities, enabling innovative teaching methodologies and bridging the gap between educational psychology and dance pedagogy (Gamble and Johnson, 2025). By utilizing AI technologies, educators can gain deeper insights into cognitive and emotional processes, enhance learning experiences, and develop adaptive systems tailored to individual learners. Moreover, the application of large language models (LLMs) in dance education opens new avenues for personalized content creation, effective communication, and increased engagement. This evolution is not merely technological but essential for addressing the limitations of traditional approaches, ensuring that dance education remains inclusive, accessible, and effective in the modern era (Papp-Danka and Oláh, 2021).
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.
- Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
- Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
- Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
A 90-day district pilot
- 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.
- 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.
- 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.
- 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
Lan Zhao; Qi Huang; Kai Zhou; Langping Teng; Pengfei Tang. Exploring large language model applications in dance education from an educational psychology perspective. Frontiers in Psychology (2026-05-19). https://doi.org/10.3389/fpsyg.2026.1746070
Companion prepared 2026-11-01. 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.