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The Nexus of Gen‐AI Features in Higher Education: Reducing Cognitive Load, Building Resilience, and Personalising Learning.

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Title: The Nexus of Gen‐AI Features in Higher Education: Reducing Cognitive Load, Building Resilience, and Personalising Learning.
Authors: Rehman, Nadia (AUTHOR), Huang, Xiao (AUTHOR), Zhang, Yanjun (AUTHOR), Mahmood, Amir (AUTHOR)
Source: Journal of Computer Assisted Learning. Oct2026, Vol. 42 Issue 5, p1-21. 21p.
Subjects: Generative artificial intelligence, Psychological resilience, Cross-sectional method, Social sciences, Research funding, Engineering, Problem solving, Life sciences, Quantitative research, Judgment sampling, Motivation (Psychology), Business, Humanities, Learning strategies, Masters programs (Higher education), Cognition, Employees' workload, Chatbots
Geographic Terms: Pakistan, Morocco, Egypt, China, Nigeria
Abstract: Background: Generative Artificial Intelligence (Gen‐AI) is reshaping higher education by offering adaptive support, instant feedback, and greater learner autonomy. Despite the increasing use of tools such as ChatGPT in university learning, empirical evidence on how their motivational affordances influence academic resilience and personalised learning remains limited, especially among non‐native learners. Objectives: This study examines how five motivational features of Gen‐AI‐Adaptive Learning Support (ALS), Instantaneous Problem Resolution (IPR), Subject‐Specific Guidance (SSG), Temporal Flexibility (TF), and Language Accessibility (LA) influence cognitive load, academic resilience, and personalised learning. Specifically, it addresses three research questions: (1) To what extent do generative AI affordances influence cognitive load reduction among higher education students? (2) To what extent do generative AI affordances influence academic resilience and personalised learning? (3) Does cognitive load reduction mediate the relationship between generative AI affordances and academic resilience and personalised learning? Methodology: A cross‐sectional survey was conducted with 813 non‐native university students from seven higher education institutions in China. Structural equation modelling was used to test the hypothesised relationships among Gen‐AI affordances, cognitive load, academic resilience, and personalised learning. Results: All five motivational features positively influenced cognitive load reduction, personalised learning, and academic resilience. Cognitive load reduction significantly mediated these relationships, suggesting that cognitive load reduction represents an important indirect pathway linking Gen‐AI affordances with learning efficiency, academic resilience, and personalised learning. Conclusion: Gen‐AI can support personalised and resilient learning by reducing cognitive burden. This study clarifies the mediating role of cognitive processes and offers practical implications for educators and EdTech developers designing adaptive and inclusive learning environments. Summary: What is already known about this topic ○Gen‐AI tools are increasingly used in higher education to support explanation, feedback, content generation, and flexible learning.○Prior research suggests that Gen‐AI affordances such as immediacy, adaptability, and accessibility may improve student engagement and support learning.○However, limited empirical evidence explains how these affordances influence academic outcomes through cognitive processes such as cognitive load.What this paper adds ○This study provides empirical evidence from 813 non‐native university students in China on how five motivational affordances of Gen‐AI relate to cognitive load reduction, academic resilience, and personalised learning.○It shows that cognitive load reduction represents an important indirect pathway linking Gen‐AI affordances to both academic resilience and personalised learning.○It extends current research by focusing on international and linguistically diverse learners in Chinese higher education, a context that remains underrepresented in the Gen‐AI literature.Implications for Practice and/or Policy ○Educators can use Gen‐AI more strategically as a pedagogical scaffold to reduce unnecessary mental burden and support more personalised and resilient learning.○Higher education institutions should promote inclusive AI practices that support multilingual, diverse, and internationally mobile student populations.○EdTech developers and policymakers should design and regulate AI‐supported learning environments to balance accessibility, adaptability, academic integrity, and meaningful student engagement. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
Description
Abstract:Background: Generative Artificial Intelligence (Gen‐AI) is reshaping higher education by offering adaptive support, instant feedback, and greater learner autonomy. Despite the increasing use of tools such as ChatGPT in university learning, empirical evidence on how their motivational affordances influence academic resilience and personalised learning remains limited, especially among non‐native learners. Objectives: This study examines how five motivational features of Gen‐AI‐Adaptive Learning Support (ALS), Instantaneous Problem Resolution (IPR), Subject‐Specific Guidance (SSG), Temporal Flexibility (TF), and Language Accessibility (LA) influence cognitive load, academic resilience, and personalised learning. Specifically, it addresses three research questions: (1) To what extent do generative AI affordances influence cognitive load reduction among higher education students? (2) To what extent do generative AI affordances influence academic resilience and personalised learning? (3) Does cognitive load reduction mediate the relationship between generative AI affordances and academic resilience and personalised learning? Methodology: A cross‐sectional survey was conducted with 813 non‐native university students from seven higher education institutions in China. Structural equation modelling was used to test the hypothesised relationships among Gen‐AI affordances, cognitive load, academic resilience, and personalised learning. Results: All five motivational features positively influenced cognitive load reduction, personalised learning, and academic resilience. Cognitive load reduction significantly mediated these relationships, suggesting that cognitive load reduction represents an important indirect pathway linking Gen‐AI affordances with learning efficiency, academic resilience, and personalised learning. Conclusion: Gen‐AI can support personalised and resilient learning by reducing cognitive burden. This study clarifies the mediating role of cognitive processes and offers practical implications for educators and EdTech developers designing adaptive and inclusive learning environments. Summary: What is already known about this topic ○Gen‐AI tools are increasingly used in higher education to support explanation, feedback, content generation, and flexible learning.○Prior research suggests that Gen‐AI affordances such as immediacy, adaptability, and accessibility may improve student engagement and support learning.○However, limited empirical evidence explains how these affordances influence academic outcomes through cognitive processes such as cognitive load.What this paper adds ○This study provides empirical evidence from 813 non‐native university students in China on how five motivational affordances of Gen‐AI relate to cognitive load reduction, academic resilience, and personalised learning.○It shows that cognitive load reduction represents an important indirect pathway linking Gen‐AI affordances to both academic resilience and personalised learning.○It extends current research by focusing on international and linguistically diverse learners in Chinese higher education, a context that remains underrepresented in the Gen‐AI literature.Implications for Practice and/or Policy ○Educators can use Gen‐AI more strategically as a pedagogical scaffold to reduce unnecessary mental burden and support more personalised and resilient learning.○Higher education institutions should promote inclusive AI practices that support multilingual, diverse, and internationally mobile student populations.○EdTech developers and policymakers should design and regulate AI‐supported learning environments to balance accessibility, adaptability, academic integrity, and meaningful student engagement. [ABSTRACT FROM AUTHOR]
ISSN:02664909
DOI:10.1002/jcal.70312