Motivational Theories in Action: A Guide for Teaching Artificial Intelligence Prompts to Support Student Learning Motivation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it