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Behavioral Sciences
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The Role of Generative Artificial Intelligence in Supporting Student Learning...
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The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education

A special issue of Behavioral Sciences (ISSN 2076-328X). This special issue belongs to the section "Educational Psychology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 50872

Special Issue Editors

Dr. Rebecca Upsher

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Guest Editor
Department of Psychology, King’s College London, London SE1 1UL, UK
Interests: mixed-methods research; student wellbeing; curriculum; pedagogy and assessment; artificial intelligence and assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Claire Heard

E-Mail Website
Guest Editor
Department of Psychology, King’s College London, London SE1 1UL, UK
Interests: judgement and decision making; risk communication; choice architecture; inclusive education; artificial intelligence and assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Sumeyra Yalcintas

E-Mail Website
Guest Editor
Department of Psychology, King’s College London, London SE1 1UL, UK
Interests: close relationships; well-being; pedagogy; artificial intelligence and assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the availability and popularity of generative artificial intelligence (GenAI), the relationship between technology and education has evolved significantly in recent years. This Special Issue explores the multifaceted ways in which GenAI can support student learning, enhance pedagogical practices and transform the educational landscape in postsecondary education.

With its capacity to analyze data, generate content and personalize learning experiences, GenAI has the potential to reshape how students engage with course materials, interact with instructors and navigate their educational journey. The topics explored in this Special Issue aim to capture the breadth of this technological impact, from theoretical perspectives to practical applications, including the following:

  • The use of GenAI in enhancing student learning experiences: How can GenAI tools facilitate personalized learning pathways, provide feedback and support student progress?
  • Pedagogical strategies integrating GenAI: How are educators leveraging or integrating GenAI to design curriculum, deliver content and evaluate student outcomes?
  • Integration of GenAI in formative and summative assessment activities: How can AI tools be integrated into formative and summative assessment activities, and how can they support personalized feedback and understand assessment criteria?
  • Ethical considerations and challenges: What ethical considerations arise with the integration of GenAI in postsecondary education, and how can they be addressed?
  • Comparative studies: How do different educational institutions and disciplines utilize GenAI to support teaching and learning? How do different types of students (for instance, neurotypical vs. neurodiverse) experience and interact with GenAI tools?

This Special Issue welcomes empirical research, qualitative, quantitative or mixed-methods analysis, systematic reviews and case studies that explore the integration of GenAI in postsecondary education.

We hope that this Special Issue will promote dialogue and innovation, and provide a platform for exploring the transformative potential of GenAI in postsecondary education.

You may choose our Joint Special Issue in Education Sciences.

Dr. Rebecca Upsher
Dr. Claire Heard
Dr. Sumeyra Yalcintas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Behavioral Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • generative artificial intelligence
  • pedagogy
  • postsecondary
  • higher education
  • curriculum design
  • student learning
  • assessment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

21 pages, 1986 KiB  
Article
Complementing but Not Replacing: Comparing the Impacts of GPT-4 and Native-Speaker Interaction on Chinese L2 Writing Outcomes
by Zhaoyang Shan, Zhangyuan Song, Xu Jiang, Wen Chen and Luyao Chen
Behav. Sci. 2025, 15(4), 540; https://doi.org/10.3390/bs15040540 - 17 Apr 2025
Viewed by 554
Abstract
This study explored the efficacy of large language models (LLMs), namely GPT-4, in supporting second language (L2) writing in comparison with interaction with a human language partner in the pre-writing phase. A within-subject behavioral experiment was conducted with 23 Chinese L2 learners who [...] Read more.
This study explored the efficacy of large language models (LLMs), namely GPT-4, in supporting second language (L2) writing in comparison with interaction with a human language partner in the pre-writing phase. A within-subject behavioral experiment was conducted with 23 Chinese L2 learners who were exposed to three conditions: “without interaction”, “interaction with GPT-4”, and “interaction with a language partner”. They then completed an L2 writing task. It was found that interaction with the language partner yielded significantly improved results compared with both interaction with GPT-4 and the case without interaction in terms of overall writing scores, organization, and language. Additionally, both types of interaction enhanced the participants’ topic familiarity and writing confidence and reduced the task’s perceived difficulty compared with the case without interaction. Interestingly, in the “interaction with GPT-4” condition, topic familiarity was positively correlated with better writing outcomes, whereas in the “interaction with a language partner” condition, perceived difficulty was positively correlated with content scores; however, content scores were negatively associated with writing confidence. This study suggests that LLMs should be used to complement and not replace human language partners in the L2 pre-writing phase. Full article
(This article belongs to the Special Issue The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education)
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24 pages, 1414 KiB  
Article
Artificial Intelligence in Higher Education: The Impact of Need Satisfaction on Artificial Intelligence Literacy Mediated by Self-Regulated Learning Strategies
by Kai Wang, Wencheng Cui and Xue Yuan
Behav. Sci. 2025, 15(2), 165; https://doi.org/10.3390/bs15020165 - 2 Feb 2025
Cited by 2 | Viewed by 3472
Abstract
Artificial intelligence (AI) technologies have profoundly influenced both professional environments and personal lives. In the rapidly developing sector of AI education, fostering essential AI literacy among university students has become vital. Nevertheless, the factors that determine AI literacy remain insufficiently defined. This research, [...] Read more.
Artificial intelligence (AI) technologies have profoundly influenced both professional environments and personal lives. In the rapidly developing sector of AI education, fostering essential AI literacy among university students has become vital. Nevertheless, the factors that determine AI literacy remain insufficiently defined. This research, grounded in self-determination theory (SDT), seeks to investigate the relationships among three components: the fulfillment of university students’ three psychological needs, self-regulated learning strategies (SRLSs), and AI literacy. The aim is to enhance human capital efficiency and prepare students to tackle future workplace challenges effectively. To examine these connections, a cross-sectional survey was administered to 1056 university students. The findings reveal that satisfying the three psychological needs—perceived autonomy, competence, and relatedness—plays a pivotal role in advancing AI literacy among university students. Additionally, four SRLSs—cognitive engagement, metacognitive knowledge, resource management, and motivational beliefs—acted as mediators between these psychological needs and AI literacy. Consequently, this study not only enhances our understanding of the psychological and behavioral development of university students during their engagement with AI education but also provides theoretical support and practical guidance for fostering their AI literacy. Full article
(This article belongs to the Special Issue The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education)
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38 pages, 3424 KiB  
Article
Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics
by Yang Zhang and Changqi Dong
Behav. Sci. 2024, 14(11), 1015; https://doi.org/10.3390/bs14111015 - 31 Oct 2024
Cited by 1 | Viewed by 2198
Abstract
The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel [...] Read more.
The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human–computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era. Full article
(This article belongs to the Special Issue The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education)
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16 pages, 1143 KiB  
Article
The Impact of AI Usage on University Students’ Willingness for Autonomous Learning
by Ling Wang and Wenye Li
Behav. Sci. 2024, 14(10), 956; https://doi.org/10.3390/bs14100956 - 16 Oct 2024
Cited by 9 | Viewed by 20961
Abstract
As artificial intelligence (AI) technology becomes increasingly integrated into education, understanding the theoretical mechanisms that drive university students to adopt new learning behaviors through these tools is essential. This study extends the Expectation-Confirmation Model (ECM) by incorporating both cognitive and affective variables to [...] Read more.
As artificial intelligence (AI) technology becomes increasingly integrated into education, understanding the theoretical mechanisms that drive university students to adopt new learning behaviors through these tools is essential. This study extends the Expectation-Confirmation Model (ECM) by incorporating both cognitive and affective variables to examine students’ current AI usage and their future expectations. The model includes intrinsic and extrinsic motivations, focusing on three key factors: positive emotions, digital efficacy, and willingness for autonomous learning. A survey of 721 valid responses revealed that positive emotions, digital efficacy, and satisfaction significantly influence continued AI usage, with positive emotions being particularly critical. Digital efficacy and perceived usefulness also impact satisfaction, but long-term usage intentions are more effectively driven by positive emotions. Furthermore, digital efficacy strongly affects the willingness for autonomous learning. Therefore, higher education institutions should promote AI technology, enhance students’ expectation-confirmation levels, and emphasize positive emotional experiences during AI use. Adopting a “human–machine symbiosis” model can foster active learning, personalized learning pathways, and the development of students’ digital efficacy and innovation capabilities. Full article
(This article belongs to the Special Issue The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education)
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22 pages, 814 KiB  
Article
ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy
by Ahlam Mohammed Al-Abdullatif and Merfat Ayesh Alsubaie
Behav. Sci. 2024, 14(9), 845; https://doi.org/10.3390/bs14090845 - 19 Sep 2024
Cited by 11 | Viewed by 12332
Abstract
This study sought to understand students’ intentions regarding the use of ChatGPT in learning from the perspective of perceived value, exploring the influence of artificial intelligent (AI) literacy. Drawing on a sample of 676 university students from diverse academic backgrounds, we employed a [...] Read more.
This study sought to understand students’ intentions regarding the use of ChatGPT in learning from the perspective of perceived value, exploring the influence of artificial intelligent (AI) literacy. Drawing on a sample of 676 university students from diverse academic backgrounds, we employed a structured survey questionnaire to measure their perceptions of ChatGPT as a learning tool. The collected data were then analyzed using structural equation modeling (SEM) via SmartPLS 4 software. The findings showed a strong effect of the students’ perceived value of ChatGPT on their intention to use it. Our findings suggest that perceived usefulness, perceived enjoyment and perceived fees had a significant influence on students’ perceived value of ChatGPT, while perceived risk showed no effect. Moreover, the role of AI literacy emerged as pivotal in shaping these perceptions. Students with higher AI literacy demonstrated an enhanced ability to discern the value of ChatGPT. AI literacy proved to be a strong predictor of students’ perception of usefulness, enjoyment, and fees for using ChatGPT in learning. However, AI literacy did not have an impact on students’ perceptions of using ChatGPT in learning. This study underscores the growing importance of integrating AI literacy into educational curricula to optimize the reception and utilization of innovative AI tools in academic scenarios. Future interventions aiming to boost the adoption of such tools should consider incorporating AI literacy components to maximize perceived value and, subsequently, use intention. Full article
(This article belongs to the Special Issue The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education)
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18 pages, 666 KiB  
Article
Unpacking the Dynamics of AI-Based Language Learning: Flow, Grit, and Resilience in Chinese EFL Contexts
by Xiuwen Zhai, Ruijie Zhao, Yueying Jiang and Hanwei Wu
Behav. Sci. 2024, 14(9), 838; https://doi.org/10.3390/bs14090838 - 19 Sep 2024
Cited by 8 | Viewed by 4839
Abstract
Artificial intelligence and positive psychology play crucial roles in education, yet there is limited research on how these psychological factors influence learners’ use of AI, particularly in language education. Grounded in self-determination theory, this study investigates the factors influencing Chinese English learners’ intention [...] Read more.
Artificial intelligence and positive psychology play crucial roles in education, yet there is limited research on how these psychological factors influence learners’ use of AI, particularly in language education. Grounded in self-determination theory, this study investigates the factors influencing Chinese English learners’ intention to use AI for language learning. Utilizing structural equation modeling, this research examines the mediating roles of grit, flow, and resilience in the relationship between basic psychological needs and the intention to use AI. Data were analyzed using AMOS 26 and SPSS 26. The findings reveal that flow, grit, and resilience mediate the relationship between basic psychological needs and the intention to adopt AI tools for language learning. This study provides valuable insights into how educational environments can be designed to fulfill psychological needs, thereby fostering greater engagement and acceptance of AI in language education. Full article
(This article belongs to the Special Issue The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education)
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21 pages, 2478 KiB  
Article
Training and Technology Acceptance of ChatGPT in University Students of Social Sciences: A Netcoincidental Analysis
by Elena María García-Alonso, Ana Cristina León-Mejía, Roberto Sánchez-Cabrero and Raquel Guzmán-Ordaz
Behav. Sci. 2024, 14(7), 612; https://doi.org/10.3390/bs14070612 - 18 Jul 2024
Cited by 7 | Viewed by 4001
Abstract
This study analyzes the perception and usage of ChatGPT based on the technology acceptance model (TAM). Conducting reticular analysis of coincidences (RAC) on a convenience survey among university students in the social sciences, this research delves into the perception and utilization of this [...] Read more.
This study analyzes the perception and usage of ChatGPT based on the technology acceptance model (TAM). Conducting reticular analysis of coincidences (RAC) on a convenience survey among university students in the social sciences, this research delves into the perception and utilization of this artificial intelligence tool. The analysis considers variables such as gender, academic year, prior experience with ChatGPT, and the training provided by university faculty. The networks created with the statistical tool “CARING” highlight the role of perceived utility, credibility, and prior experience in shaping attitudes and behaviors toward this emerging technology. Previous experience, familiarity with video games, and programming knowledge were related to more favorable attitudes towards ChatGPT. Students who received specific training showed lower confidence in the tool. These findings underscore the importance of implementing training strategies that raise awareness among students about both the potential strengths and weaknesses of artificial intelligence in educational contexts. Full article
(This article belongs to the Special Issue The Role of Generative Artificial Intelligence in Supporting Student Learning in Postsecondary Education)
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