AI applications in STEM education foster positive affective perception, including attitude, interest, and motivation¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A majority of reviewed studies (N = 17) revealed effects of AI technologies on students' affective perception such as attitude, interest, and motivation, with students showing satisfaction and positive attitudes. → Weiqi Xu and Fan Ouyang 2022

Evidence¶

Weiqi Xu and Fan Ouyang 2022¶

Weiqi Xu and Fan Ouyang. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education. https://doi.org/10.1186/s40594-022-00377-5

q2 · i? · review · r2

The review's synthesis reports that 17 studies examined affective outcomes. Students showed satisfaction and positive attitude towards the integration of AI technologies and STEM education, and AI applications aroused students' interests and motivation, e.g., positive feedback towards learning analytics in programming classes and a personalized learning environment engaging engineering students with strong interest and motivation.

"a majority of studies revealed the educational effects of AI technologies on students’ affective percep- tion, such as attitude, interest, and motivation (N = 17)."

Discussion¶

Related Claims¶

• Educational robotics has a moderate effect on students' STEM learning attitudes — a narrower finding that bears on this claim
• Students in secondary mathematics report high acceptance of the domain-specific AI chatbot ADA across all Technology Acceptance Model dimensions — a narrower finding that bears on this claim
• Students hold generally positive attitudes toward Gen AI-assisted problem posing, with a minority mixed or negative — a narrower finding that bears on this claim
• Gender, age and English proficiency show nuanced group differences in chatbot attitudes and concerns — related
• Teachers' expectancy beliefs about successfully using AI applications shape their motivation to use them (theoretical argument). — related
• Review reports that teachers' beliefs, attitudes and TPACK—not AI knowledge alone—mediate AI integration — related
• 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). — related
• Teachers hold conflicting attitudes about whether gameful assessments support students’ intrinsic motivation and desire to learn — related
• EAI modalities were associated with higher student motivation and changes in attitudes toward STEM knowledge areas — possibly the same claim (merge candidate)