Post-interaction reflections reveal four preference themes: AI as formative learning aid, preference for human authority, conditional acceptance, and skepticism about educational impact¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Reflexive thematic analysis of 63 reflective responses identified four preference-oriented themes, with most respondents expressing nuanced rather than absolute positions. → Yildirim-Erbasli 2026 q2 i? Sentiment analysis and LDA topic modelling showed positive sentiment terms occurred more frequently than negative terms across all three topics. → Yildirim-Erbasli 2026 (2)

Evidence¶

Yildirim-Erbasli 2026¶

Yildirim-Erbasli, S. N., Ilgun Dibek, M., Thomas, M. L., & Lesoway, N. (2026). When Do Undergraduate Students Prefer AI? Insights into AI Scoring and Feedback. Behavioral Sciences, 16(7), 1196. https://doi.org/10.3390/bs16071196

q2 · i? · qualitative · r2

Reflexive thematic analysis of reflections from 63 of the 93 participants after generating AI scoring and feedback with ChatGPT. Each response was coded with a maximum of two themes; "Most respondents expressed nuanced positions rather than absolute support or rejection."

"Thematic analysis of reflections revealed four distinct preference-oriented themes regarding AI scoring and feedback: AI as a formative learning aid, preference for human authority, conditional or context-dependent acceptance, and skepticism about its educational impact"

Yildirim-Erbasli 2026 (2)¶

Yildirim-Erbasli, S. N., Ilgun Dibek, M., Thomas, M. L., & Lesoway, N. (2026). When Do Undergraduate Students Prefer AI? Insights into AI Scoring and Feedback. Behavioral Sciences, 16(7), 1196. https://doi.org/10.3390/bs16071196

q2 · i? · associational · r2

Context-specific sentiment analysis with a custom lexicon and LDA topic modelling (three topics) on the same reflective responses. Topics were detailed feedback and examples, usefulness and clarity, and accuracy of scoring and feedback; positive terms dominated across all topics.

"Across all topics, positive sentiment-related terms occurred more frequently than negative terms, resulting in higher positive sentiment scores."

Discussion¶

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