Unveiling AI Perceptions: How Student Attitudes Towards AI Shape AI Awareness, Usage, and Conceptions

This study examines the relationship between students’ attitudes toward artificial intelligence (AI) and both AI competence and conceptions. 176 UK university students completed a survey where they were asked to rate statements in relation to their attitudes towards AI, their AI competence and their conceptions about AI using 5-point Likert-type scales. In relation to AI competence, results indicate that affective attitudes predicted awareness and usage, leading to information avoidance and disengagement. Cognitive attitudes positively predicted AI awareness and usage. Behavioural attitudes, however, did not predict awareness or usage, suggesting that individuals may engage with AI technology without deeper understanding. For AI conceptions, behavioural attitudes were more closely linked to conceptions of AI in educational contexts. Positive behavioural attitudes predicted students’ conceptions of AI’s role in intelligent tutoring systems, retentions, drop-out reduction, recommendation systems, and personalised learning. In contrast, affective attitudes predicted conceptions of AI’s use in classroom monitoring and performance prediction, while cognitive attitudes had little influence. These are areas educators can focus on when designing teaching & assessment strategies in relation to AI.

Authors

Institutions

Publication Details

Journal
International Journal of Technology in Education
Published
2025-01-09
DOI
https://doi.org/10.46328/ijte.995
Citations
10
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
24.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unveiling AI Perceptions: How Student Attitudes Towards AI Shape AI Awareness, Usage, and Conceptions

Stephanie Baines, Pauldy Otermans, Charlotte Roberts
10 citations
International Journal of Technology in Education
Explainable Artificial Intelligence (XAI)
24.81
article

Unveiling AI Perceptions: How Student Attitudes Towards AI Shape AI Awareness, Usage, and Conceptions

Stephanie Baines, Pauldy Otermans, Charlotte Roberts
article en
10 citations

Abstract

This study examines the relationship between students’ attitudes toward artificial intelligence (AI) and both AI competence and conceptions. 176 UK university students completed a survey where they were asked to rate statements in relation to their attitudes towards AI, their AI competence and their conceptions about AI using 5-point Likert-type scales. In relation to AI competence, results indicate that affective attitudes predicted awareness and usage, leading to information avoidance and disengagement. Cognitive attitudes positively predicted AI awareness and usage. Behavioural attitudes, however, did not predict awareness or usage, suggesting that individuals may engage with AI technology without deeper understanding. For AI conceptions, behavioural attitudes were more closely linked to conceptions of AI in educational contexts. Positive behavioural attitudes predicted students’ conceptions of AI’s role in intelligent tutoring systems, retentions, drop-out reduction, recommendation systems, and personalised learning. In contrast, affective attitudes predicted conceptions of AI’s use in classroom monitoring and performance prediction, while cognitive attitudes had little influence. These are areas educators can focus on when designing teaching & assessment strategies in relation to AI.

International Journal of Technology in EducationVol. 8(1)
Brunel University of London (GB)
Openalex Percentile: Top 1%
Explainable Artificial Intelligence (XAI)
24.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.