The Psychological Effects of AI-Based Learning on Student Motivation, Anxiety, and Cognitive Load

This research examines the psychological impact of AI-based learning on student motivation, anxiety, and cognitive load in a sample of 268 Sindh and Punjab students. The results show a weak but significant relationship between AI-based learning and motivation (r =.305, p <.01), suggesting that AI encourages engagement but is not the only cause of motivation. Additionally, AI-based learning shows a moderate relationship with student anxiety (r =.305, p <.01), suggesting that AI-based assessment and feedback are causes of increased stress. A weak relationship between AI-based learning and cognitive load (r =.167, p <.01) suggests that AI is a cause of students' mental load, but to a lesser degree. The regression analysis (R² =.007, p =.179) validates that AI-based learning in isolation is not a good predictor of student anxiety. These results highlight the importance of an equilibrium-based integration of AI, personalized learning strategies, and stress management programs to attain maximum AI-based education. Future research should aim to explore the longitudinal effects and cross-cultural differences of AI-based learning experiences.

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Publication Details

Journal
˜The œcritical review of social sciences studies
Published
2025-03-21
DOI
https://doi.org/10.59075/1fvs0v71
Citations
1
Primary Topic
Education and Learning Interventions
Type
article
Field-Weighted Citation Impact
3.25
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article

The Psychological Effects of AI-Based Learning on Student Motivation, Anxiety, and Cognitive Load

Kiran Shafiq Khan, Anam Khan, M.Yusuf Aziz, Marium Minhas
1 citations
˜The œcritical review of social sciences studies
Education and Learning Interventions
3.25
article

The Psychological Effects of AI-Based Learning on Student Motivation, Anxiety, and Cognitive Load

Kiran Shafiq Khan, Anam Khan, M.Yusuf Aziz, Marium Minhas
article en
1 citations

Abstract

This research examines the psychological impact of AI-based learning on student motivation, anxiety, and cognitive load in a sample of 268 Sindh and Punjab students. The results show a weak but significant relationship between AI-based learning and motivation (r =.305, p <.01), suggesting that AI encourages engagement but is not the only cause of motivation. Additionally, AI-based learning shows a moderate relationship with student anxiety (r =.305, p <.01), suggesting that AI-based assessment and feedback are causes of increased stress. A weak relationship between AI-based learning and cognitive load (r =.167, p <.01) suggests that AI is a cause of students' mental load, but to a lesser degree. The regression analysis (R² =.007, p =.179) validates that AI-based learning in isolation is not a good predictor of student anxiety. These results highlight the importance of an equilibrium-based integration of AI, personalized learning strategies, and stress management programs to attain maximum AI-based education. Future research should aim to explore the longitudinal effects and cross-cultural differences of AI-based learning experiences.

˜The œcritical review of social sciences studiesVol. 3(1)
Public Policy Institute of California (US), Grammar School (SK), Bahria University (PK), University of Malakand (PK)
Openalex Percentile: Top 10%
Education and Learning Interventions
3.25
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