Cognitive Effort Displacement in AI-Assisted Learning: Effects on Student Engagement and Knowledge Retention on TVET

Artificial intelligence (AI) is increasingly transforming teaching and learning in Technical and Vocational Education and Training (TVET) by providing personalized instructional support and improving access to learning resources. However, growing reliance on AI tools may reduce learners’ cognitive effort, leading to cognitive displacement with potential implication for student engagement and knowledge retention. This study investigated the extent of AI tool usage, the level of cognitive effort displacement, and the relationships among AI tool usage, cognitive effort displacement, student engagement, and knowledge retention among TVET students in Abia State, Nigeria. A descriptive correlational survey design was adopted. The study population comprised 2,400 TVET students, from which a sample of 343 respondents was selected using multistage sampling techniques. Data were collected using the Cognitive Effort Displacement, Engagement, and Retention Questionnaire (CEDERQ) (α = 0.85). Mean and standard deviation were used to answer the research questions, while Pearson product-moment correlation was employed to test the hypotheses at the 0.05 level of significance. The findings revealed a high level of AI tool usage (x̄ = 3.47), a moderate to high level of cognitive effort displacement (x̄ = 3.26), and a high level of student engagement and knowledge retention (x̄ = 3.32). AI tool usage showed a significant positive relationship with cognitive effort displacement (r = 0.612, p < 0.05), whereas cognitive effort displacement had significant negative relationships with student engagement (r = –0.478, p < 0.05) and knowledge retention (r = –0.563, p < 0.05). The study concludes that while AI-assisted learning enhances instructional support in TVET, excessive dependence on AI may undermine active cognitive engagement and long-term knowledge retention. The study recommends pedagogical approaches that balance AI support with independent problem-solving and reflective learning.

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Journal
Iconic Research and Engineering Journals
Published
2026-10-30
DOI
https://doi.org/10.64388/ire1724517
Primary Topic
Artificial Intelligence in Education
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article
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article

Cognitive Effort Displacement in AI-Assisted Learning: Effects on Student Engagement and Knowledge Retention on TVET

Ndukwe Igwe, Dr. Aboajah Chisomaga, Onyekachi Kevin Attah, Samuel Chukwuma Chinonso et al.
5 links
Iconic Research and Engineering Journals
Artificial Intelligence in Education
article

Cognitive Effort Displacement in AI-Assisted Learning: Effects on Student Engagement and Knowledge Retention on TVET

Ndukwe Igwe, Dr. Aboajah Chisomaga, Onyekachi Kevin Attah, Samuel Chukwuma Chinonso, Kalu Ogbonna
article en

Abstract

Artificial intelligence (AI) is increasingly transforming teaching and learning in Technical and Vocational Education and Training (TVET) by providing personalized instructional support and improving access to learning resources. However, growing reliance on AI tools may reduce learners’ cognitive effort, leading to cognitive displacement with potential implication for student engagement and knowledge retention. This study investigated the extent of AI tool usage, the level of cognitive effort displacement, and the relationships among AI tool usage, cognitive effort displacement, student engagement, and knowledge retention among TVET students in Abia State, Nigeria. A descriptive correlational survey design was adopted. The study population comprised 2,400 TVET students, from which a sample of 343 respondents was selected using multistage sampling techniques. Data were collected using the Cognitive Effort Displacement, Engagement, and Retention Questionnaire (CEDERQ) (α = 0.85). Mean and standard deviation were used to answer the research questions, while Pearson product-moment correlation was employed to test the hypotheses at the 0.05 level of significance. The findings revealed a high level of AI tool usage (x̄ = 3.47), a moderate to high level of cognitive effort displacement (x̄ = 3.26), and a high level of student engagement and knowledge retention (x̄ = 3.32). AI tool usage showed a significant positive relationship with cognitive effort displacement (r = 0.612, p < 0.05), whereas cognitive effort displacement had significant negative relationships with student engagement (r = –0.478, p < 0.05) and knowledge retention (r = –0.563, p < 0.05). The study concludes that while AI-assisted learning enhances instructional support in TVET, excessive dependence on AI may undermine active cognitive engagement and long-term knowledge retention. The study recommends pedagogical approaches that balance AI support with independent problem-solving and reflective learning.

Iconic Research and Engineering JournalsVol. 10(5)
Abia State University (NG), Funai Electric (Japan) (JP), Alex Ekwueme Federal University, Ndufu-Alike (NG)
Quality education, Good health and well-being
Openalex Percentile: Top 6%
Artificial Intelligence in Education
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