Self-determination Theory 🚂 (Deci & Ryan, 1980)
Self-regulated Learning 🎒 (Zimmerman, 1986)
Technology Acceptance Model 📱 (Davis, 1989)
Expectancy-Value Theory 📈 (Wigfield & Eccles, 2000)
UTAUT 👍 (Vinkatesh, 2003)
🦹 Database limited to ERIC
🦹 Only academic journals
🦹 Articles from 2023-2024
🦹 Search criteria: (Generative AI OR GenAI OR ChatGPT) AND (Motivation OR SDT OR TAM OR Academic Integrity) AND (assignments AND high school)
🧲 700 articles found from query
🧲 ASReview of Title & Abstract produced 37 results, further reduced to 10
🧲 Additional results from Ebscohost search query added
🧲 Criteria: Generative AI and motivation from students to use the AI
🧲 Mostly quantitative/survey, some mixed methods
💥 AI ethics is a necessary discussion for high school and college students
💥 Consider how other forms of learning can be perceived as useful to reduce academic burden while bolstering academic growth
💥 Listen to student voices!
🏋️♂️ Emerging literature in a new field
🏋️♂️ Only one database
🏋️♂️ Small sample size
🏋️♂️ Lack of experimental designs
🏋️♂️ Search criteria may have limited results
Method: Quasi-randomized assignment of participants to three task framing conditions:
• Efficiency-focused: Complete tasks quickly with AI assistance.
• Learning-focused: Use AI to deepen understanding.
• Neutral: No framing, open-ended use of AI.
Intervention: Assign participants challenging tasks (e.g., essays, problem-solving) while tracking AI usage patterns (frequency, query type) and measuring outcomes (time, accuracy, quality).
Outcome: Identify discrepancies between intentions and actions, revealing how framing influences AI engagement and informing policies for ethical, meaningful AI use.