Teachers who believe AI will help them reach professional goals and improve students' learning outcomes (utility value) may be more motivated to use it (theoretical argument).¶

Claim · All claims
Evidence · 1 study (2 entries) · theoretical r2 · q1
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person
Evidence quality · No study behind this claim is a peer-reviewed article with a DOI: it rests on an ERIC document (a report, conference paper or thesis). It is kept because 2 design pages cite it; treat it as a lead to check, not an established finding.

Subclaims¶

q1 i? The paper argues that practical benefits of AI, such as enhanced classroom management, easier lesson planning and insight into students' progress, can increase teachers' motivation to use it. → Yurt 2024 q1 i? The paper argues that when teachers believe AI will help them achieve professional goals and improve students' learning outcomes, they may be more motivated to use it. → Yurt 2024 (2)

Evidence¶

Yurt 2024¶

Yurt, E. (2024). Theoretical Examination of Teachers' Motivations for Using Artificial Intelligence Applications in Education: Expectancy-Value Theory Perspective. In M. S. Ozturk, A. Kaban, & M. Unal (Eds.), Proceedings of ICSES 2024-- International Conference on Studies in Education and Social Sciences (pp. 149-158), Istanbul, Turkiye. ISTES. https://eric.ed.gov/?id=ED673212

q1 · i? · theoretical · r2

Theoretical argument in the utility value section, citing prior work (Cheng et al., 2020; Ottenbreit-Leftwich et al., 2010) rather than new data: "These practical benefits can increase teachers' motivation to use AI technologies".

"AI technologies can enhance classroom management, facilitate lesson planning, and provide teachers with valuable insights into students' learning progress. These practical benefits can increase teachers' motivation to use AI technologies (Cheng et al., 2020; Ottenbreit-Leftwich et al., 2010)."

Yurt 2024 (2)¶

Yurt, E. (2024). Theoretical Examination of Teachers' Motivations for Using Artificial Intelligence Applications in Education: Expectancy-Value Theory Perspective. In M. S. Ozturk, A. Kaban, & M. Unal (Eds.), Proceedings of ICSES 2024-- International Conference on Studies in Education and Social Sciences (pp. 149-158), Istanbul, Turkiye. ISTES. https://eric.ed.gov/?id=ED673212

q1 · i? · theoretical · r2

Theoretical argument summarising the utility value section, with no data: when AI is believed to "help them achieve their professional goals and improve students' learning outcomes", teachers may be more motivated to use it.

"When teachers believe that using AI technologies will help them achieve their professional goals and improve students' learning outcomes, they may be more motivated to use these technologies."

Studies bearing on this claim¶

Do teachers who see AI as useful, enjoyable or important to their work intend to use it more, and does that intention become use? This table gathers the studies behind this claim and the 9 narrower claims that bear on it: 5 distinct studies (3 associational, 1 theoretical, 1 causal), q1–q2, 3 of 5 with an effect size coded. One row per study, codes as each entry records them; no effect is averaged and no verdict is drawn. Built by scripts/synthesize_claim.py from eval/synthesis/; a row that looks wrong is fixed on the claim it comes from.

Discussion¶

Related Claims¶

• Teacher Attainment Value Increases Motivation To Use Ai
• Instruction should present the learning task as engaging and meaningful and promote positive expectations of success, because effort requires that learners value the task and believe they can succeed — related
• Teachers' expectancy beliefs about successfully using AI applications shape their motivation to use them (theoretical argument). — related
• Teachers who find using AI enjoyable, engaging and satisfying (intrinsic/interest value) may be more motivated to use it (theoretical argument). — related
• Teachers who perceive the challenges and barriers of using AI as high (cost) may be less motivated to use it (theoretical argument). — related
• Learners invest more effort in tasks they perceive as interesting, personally important, or useful to their future goals. — a broader claim this one bears on
• Educators moved from awareness to interest in integrating the studio's technology into their coursework — a narrower finding that bears on this claim
• AI applications in STEM education foster positive affective perception, including attitude, interest, and motivation — related
• Perceived instrumentality enhances student motivation only for students with a positive attitude toward their personal future; for students with a negative future outlook, high utility has the opposite effect — related
• The teacher behaviours most predictive of engagement change do not neatly fit within any single motivational theory, supporting theoretical integration — related
• Teachers experiencing higher average demand across lesson planning, assessment/marking, and special-education adaptation report higher odds of AI adoption — related
• Ghanaian pre-service science teachers hold significantly positive performance expectancy and effort expectancy toward AI — related
• Chatbot integration in a smart classroom is expected by the authors to increase learner engagement and academic achievement — related