Motivational Theories in Action: A Guide for Teaching Artificial Intelligence Prompts to Support Student Learning Motivation
Bibliographic record
Abstract
This conceptual study explores how motivational theories can guide the use of generative Artificial Intelligence (AI) tools, such as ChatGPT, to enhance student learning motivation. Drawing on Self-Determination Theory (SDT), Expectancy-Value Theory (EVT), and Mindset Theory (MT), we introduce the Motivation Construction Model (MCM), a theoretical framework consisting of three interrelated phases: contemplation, goal setting & planning, and action. We demonstrate how MCM can be applied in AI-driven learning environments to support personalized prompts, targeted feedback, and adaptive guidance to motivate learning. We propose that MCM is a strategic and holistic approach to equip educators with actionable guidelines to use AI for motivating students while adhering to ethical pedagogical principles. Although the MCM framework is grounded in established motivational theories, its real-world application remains to be explored. Future research should examine the effectiveness of MCM in authentic classroom contexts to better understand its potential for enhancing student motivation and informing evidence-based instructional practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".