Cited by 205Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationIntelligent Tutoring Systems and Adaptive Learning

The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.

Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education

Shahzad, Muhammad Farrukh, Xu, Shuo, Zahid, Hira·2025·Education and Information Technologies

DOI 10.1007/s10639-024-12949-9

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/s10639-024-12949-9

Cites

89 of 89 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Common method biases in behavioral research: A critical review of the literature and recommended remedies
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Perceived Behavioral Control, Self‐Efficacy, Locus of Control, and the Theory of Planned Behavior 1
Ajzen, I. (2002) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.2002.tb00236.x · Cited by 7,629
Computer Self-Efficacy: Development of a Measure and Initial Test
Compeau, D. R., & Higgins, C. A. (1995) · MIS Quarterly · doi:10.2307/249688 · Cited by 6,553
Assessing measurement model quality in PLS-SEM using confirmatory composite analysis
Hair, J. F., Howard, M. C., & Nitzl, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.11.069 · Cited by 5,446
Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory
Fornell, C., & Bookstein, F. L. (1982) · Journal of Marketing Research · doi:10.2307/3151718 · Cited by 4,645
Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452
Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · cited 1×
“The teachers and administrators in HE should consider the utilization of generative AI technologies to enhance student learning (Baidoo-Anu & Owusu Ansah, 2023 ; Shaofeng et al., 2023 ).”
GPT-3: Its Nature, Scope, Limits, and Consequences
Floridi, L., & Chiriatti, M. (2020) · Minds and Machines · doi:10.1007/s11023-020-09548-1 · Cited by 2,519

Flagged first, then by citations

Show all 88 references

Research works printed but not linked: 1

Hartati, R., & Manullang, E. B. (2024). Implementation of Telegram Chatbot AI with Natural Language Processing (NLP) in learning creative entrepreneurship to develop students’ creative and innovative competence. TALENTA Conference Series, 7(2654–7066), 73–79. https://doi.org/10.32734/lwsa.v7i2.2055 · DOI

Sorted by citations

Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105224 · Cited by 531
The effects of generative AI on collaborative problem-solving and team creativity performance in digital story creation: an experimental study
Wei, X., Wang, L., Lee, L., & Liu, R. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00526-0 · Cited by 179
“Students with limited experience with GAI tools might have relied on their initial impressions or existing beliefs, thereby shaping attitudes and intentions (Shahzad et al., 2024).”
Factors affecting generative artificial intelligence, such as ChatGPT , use in higher education: An application of technology acceptance model
Shahzad, M. F., Xu, S., & Asif, M. (2025) · British Educational Research Journal · doi:10.1002/berj.4084 · Cited by 103
Self-citation
Determinants of ChatGPT Adoption Intention in Higher Education: Expanding on TAM with the Mediating Roles of Trust and Risk
Balaskas, S., Tsiantos, V., Chatzifotiou, S., & Rigou, M. (2025) · Information · doi:10.3390/info16020082 · Cited by 81
“Essentially, while the TAM can set a ﬁrm foundation for technology adoption research, the trust and risk constructs reﬂect the unique obstacles and opportunities posed by AI technologies like ChatGPT [6,49,50].”
Influential
Exploring the impact of generative artificial intelligence on students’ learning outcomes: a meta-analysis
Zhu, Y., Liu, Q., & Zhao, L. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13420-z · Cited by 101
In GenAI we trust: An investigation of university students’ reliance on and resistance to generative AI in language learning.
Wang, F., Li, N., Cheung, A. C. K., & Wong, G. K. W. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00547-9 · Cited by 69
“In GenAI-assisted language learning, trust in GenAI impacts learners’ confidence and subsequent decision to use the technology, while a lack of trust leads to skepticism and reduces the likelihood of use (Shahzad et al., 2024).”
Influential
From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
Allison, J., Hwang, G., Mayer, R. E., Πέλλας, Ν., Karnalim, O., de Freitas, S., Ng, O., Huang, Y., Hooshyar, D., Seidman, R. H., et al. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251359964 · Cited by 71
“Much of the existing literature ﬁ xates narrowly on easily measurable outcome metrics following Generative AI tool implementation: improved test scores, completion rates, or satisfaction indices (Lin et al., 2025; Shahzad et al., 2025; Wang et al., 2024).”
Strengthening Human Epistemic Agency in the Symbiotic Learning Partnership With Generative Artificial Intelligence
Wu, J., Lee, Y., Chai, C. S., & Tsai, C. (2025) · Educational Researcher · doi:10.3102/0013189x251333628 · Cited by 81
“…learners’ epistemic stances, prior knowledge, and justification for knowing, other important characteristics, such as motivational structure, interest, and self-efficacy, may also play a critical role in shaping human-GenAI interactions (Abbas et al., 2024; Chan & Lee, 2023; Shahzad et al., 2025).”
Student Perceptions of AI-Assisted Writing and Academic Integrity: Ethical Concerns, Academic Misconduct, and Use of Generative AI in Higher Education
Lund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025) · AI in Education · doi:10.3390/aieduc1010002 · Cited by 64
“For students—particularly at the undergraduate level—GenAI has been described as a kind of always-on tutor: one that explains, rewrites, rephrases, and even reassures (Shahzad et al., 2025).”
Generative Artificial Intelligence ( ChatGPT ‐4) and Social Media Impact on Academic Performance and Psychological Well‐Being in China's Higher Education
Shahzad, M. F., Xu, S., Liu, H., & Zahid, H. (2024) · European Journal of Education · doi:10.1111/ejed.12835 · Cited by 70
Self-citation
Extending the TAM framework: Exploring learning motivation and agility in educational adoption of generative AI
Şimşek, A. S., Cengiz, G. Ş. T., & Bal, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13591-9 · Cited by 65
“Similarly, Shahzad et al. (2024) underscore the role of self-efficacy and creativity, which aligns with the contributions of MSR in fostering positive perceptions of generative AI tools.”
Developing and validating the student learning agency scale in generative artificial intelligence (AI)-supported contexts
Xia, L., Shen, K., Sun, H., An, X., & Dong, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13137-5 · Cited by 38
“This study employed purposeful sampling to gather data for developing and validating the scale, which enhanced the theoreticality and trustworthiness of the research results (Shahzad et al., 2024).”
Does Generative Artificial Intelligence Improve Students’ Higher-Order Thinking? A Meta-Analysis Based on 29 Experiments and Quasi-Experiments
Zhao, Y., Yue, Y., Sun, Z., Jiang, Q., & Li, G. (2025) · Journal of Intelligence · doi:10.3390/jintelligence13120160 · Cited by 37
“This aligns with findings by Shahzad et al. (2024), who demonstrated that the integration of Gen-AI significantly enhances the effectiveness of PBL and supports the development of student creativity.”
Generative Artificial Intelligence and Transversal Competencies in Higher Education: A Systematic Review
Deroncele-Acosta, Á., Sayán-Rivera, R. M. E., López, Á. D. M., & Norabuena-Figueroa, E. (2025) · Applied System Innovation · doi:10.3390/asi8030083 · Cited by 67
Generative AI-driven personalization of the community of inquiry model: enhancing individualized learning experiences in digital classrooms
Anderson, J. E., Nguyen, C. A., & Moreira, G. (2025) · The International Journal of Information and Learning Technology · doi:10.1108/ijilt-10-2024-0240 · Cited by 33
Do generative AI technologies play a double-edged sword role in education? Findings from hybrid approach using PLS-SEM and fsQCA
Shahzad, M. F., Xu, S., An, X., Asif, M., & Javed, I. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13528-2 · Cited by 32
“…researches discovered that students and teachers learn these critical abilities more successfully than with standard learning techniques when Gen-AI technologies that generate problem scenarios and provide iterative feedback are used (Gao et al., 2024; (Gao et al., 2024; Shahzad et al., 2024b).”
InfluentialSelf-citation
Antecedents of international entrepreneurship and emerging technologies help to achieve sustainable development goals: Moderating role of global mindset
Shahzad, M. F., & Xu, S. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123831 · Cited by 40
Self-citation
Perceptions, adoption intentions, and impacts of generative AI among Chinese university students
Zhang, R., & Wang, J. (2025) · Current Psychology · doi:10.1007/s12144-025-07928-3 · Cited by 35
“Consequently, Shahzad et al. (2024a, 2025) integrated trust theory into this framework and revealed that perceived trust moderates the relationships among PU, PEOU, and adoption intentions.”
Influential
From Perception to Practice: Artificial Intelligence as a Pathway to Enhancing Digital Literacy in Higher Education Teaching
Zuo, Z., Luo, Y., Yan, S., & Jiang, L. (2025) · Systems · doi:10.3390/systems13080664 · Cited by 29
Assessing the Impact of Strategic HR Practices on Talent Retention Through Job Satisfaction and Work Engagement: Moderating Role of Psychological Empowerment
Shahzad, M. F., Martins, J. M., Rita, J. X., Xu, S., & MUSHTAQ, H. M. (2024) · SAGE Open · doi:10.1177/21582440241281836 · Cited by 68
“Convenience sampling is often used in online surveys, where participants can be easily reached through email, social media, or other online platforms (Correia et al., 2024; Shahzad, Xu, & Zahid, 2024).”
Self-citation
Generative AI Governance Model in Educational Research
de Pinho, I. G., Costa, A. P., & Pinho, C. S. B. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1594343 · Cited by 33
What Factors Affect the Adoption Intention and Actual Use of ChatGPT in Higher Education? The Moderating Role of Academic Integrity
Subhani, F., Khan, S. A., Sandhu, M. A., & Shahzad, M. F. (2025) · TechTrends · doi:10.1007/s11528-025-01096-8 · Cited by 26
Self-citation
Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study
Lindbäck, Y., Schröder, K., Engström, T., Valeskog, K., & Sonesson, S. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07106-w · Cited by 22
“Improved AI literacy and sustainable integration can foster creativity and self-efficacy, thereby enhancing learning performance and academic achievements [6].”
Exploring enjoyment, motivation, self-efficacy, and engagement in AI-assisted English learning: A self-determination theory approach
Wang, L., & Wang, L. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102197 · Cited by 32
Exploring mechanisms of effective informal GenAI-supported second language speaking practice: a cognitive-motivational model of achievement emotions
Wu, H., & Liu, W. (2025) · Discover Computing · doi:10.1007/s10791-025-09635-w · Cited by 27
Analysing nontraditional students' ChatGPT interaction, engagement, self‐efficacy and performance: A mixed‐methods approach
Yang, M., Jiang, S., Li, B., Herman, K., Luo, T., Chappell, S., & Lovett, N. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13588 · Cited by 30
The Impact of Digital Learning Competence on the Academic Achievement of Undergraduate Students
Song, Y., Lv, S., Wang, M., Wang, Z., & Dong, W. (2025) · Behavioral Sciences · doi:10.3390/bs15070840 · Cited by 43
Does ChatGPT-Based Reading Platform Impact Foreign Language Paper Reading? Evidence from a Quasi-Experimental Study on Chinese Undergraduate Students
Zhang, Y., Lai, X., Yi, S., & Lu, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13190-0 · Cited by 18
“ChatGPT-based educational tools can give multiple responses and timely, detailed feedback to assist students in brainstorming and creatively solve research problems (He et al., 2024; Kasneci et al., 2023; Lu et al., 2023a, b), which can lead to better learning outcomes (Shahzad et al., 2024).”
Modelling economics students’ use of ChatGPT and academic performance: insights from self-determination theory and epistemic curiosity
Acquah, B. Y. S., Arthur, F., Salifu, I., Mensah, E., Opoku, E., Nortey, S. A., & Tetteh, S. A. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00373-y · Cited by 24
“SE mediates the relationship between the use of ChatGPT and academic performance, as students who create summaries using AI tools gain confidence [2, 81, 85].”
Designing artificial intelligence chatbots for self-regulated learning from a systematic review based on Habermas's three interests
Wu, X.-Y., Radloff, J., Yeter, I. H., Wang, L., & Chiu, T. K. F. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2563086 · Cited by 32
How creative self-efficacy influences problem-solving skills in engineering education: the dual mediating role of critical thinking and metacognition
Liu, Z., Guo, H., Zhou, Z., Ma, F., & Zeng, Y. (2025) · BMC Psychology · doi:10.1186/s40359-025-03630-y · Cited by 26
“Social cognitive theory suggests that individuals with higher self-efficacy are more likely to engage in proactive and strategic behaviors, which, in turn, enhance performance [39].”
Generative AI in the Learning Process: Threat or Tool? Understanding the Role of Self-Esteem and Academic Anxiety in Shaping Student Motivations
Pavone, G. (2025) · Journal of Marketing Education · doi:10.1177/02734753251346857 · Cited by 24
“Shahzad et al. (2024) ChatGPT and chatbots significantly influence students’ learning performance through self-efficacy, fairness and ethics, and creativity.”
Influential
Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach
Abreh, M. K., Arthur, F., Akwetey, F. A., & Nortey, S. A. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00466-8 · Cited by 19
“From the SDT perspective, the study confirms a strong positive relationship between self-efficacy (SE) and AI learning intention [7, 8, 68].”
Generative artificial intelligence use and employees' digital performance
Qi, Z., Zhang, X., Ma, L., & Wang, G. (2025) · Management Decision · doi:10.1108/md-09-2024-2060 · Cited by 14
The chain mediating role of critical thinking and AI self-efficacy in GenAI usage competence and engineering students’ creativity
Zhou, Z., Guo, H., Ma, F., Yang, C., & Gao, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-21132-0 · Cited by 23
“Shahzad et al. (2025) found in a study of 362 students that generative AI technologies like ChatGPT significantly influenced learning performance through self-efficacy, highlighting that competence in applying AI tools boosts students’ AI self-efficacy and learning outcomes 39 .”
Enhancing Personalised Learning and Student Engagement Using Generative AI
Owoseni, A., Kolade, O., & Egbetokun, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-60179-8_5 · Cited by 15
The Impact of Digital Safety Competence on Cognitive Competence, AI Self-Efficacy, and Character
Ma, C. M. S., Shek, D. T. L., Fan, I. Y. H., Zhu, X., & Hu, X. (2025) · Applied Sciences · doi:10.3390/app15105440 · Cited by 21
Empowering STEM teachers’ professional learning through GenAI: The roles of task-technology fit, cognitive appraisal, and emotions
Zhu, Y., Jiang, H., & Chugh, R. (2025) · Teaching and Teacher Education · doi:10.1016/j.tate.2025.105204 · Cited by 13
Gain or loss? The dual effects of dependence on AI on employee’s creativity
Cui, S., Wang, L., Cao, W., & Zhu, T. (2025) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.103001 · Cited by 15
Generative AI as a Teaching Tool for Social Research Methodology: Addressing Challenges in Higher Education
Arosio, L. (2025) · Societies · doi:10.3390/soc15060157 · Cited by 11
What Shapes Learners’ Trust in AI? A Meta-Analytic Review of Its Antecedents and Consequences
Cui, Y., Zeng, M. L., Du, X. K., & He, W. M. (2025) · IEEE Access · doi:10.1109/access.2025.3611367 · Cited by 18
“Importantly, when learners trust an AI system, they are more likely to accept its recommendations and adjust their learning strategies accordingly—leading to improvements in knowledge acquisition, critical thinking, and learning preparedness [63], [64].”
Adoption intention of generative artificial intelligence among Chinese college students: an extended TAM-UTAUT2 model from the four-helix perspective
Xiaomin, J., Kai, C., & Ping, W. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1686408 · Cited by 14
Sustained use of generative AI for shopping: a PLS-ANN analysis
Foroughi, B., Ghobakhloo, M., Wen, J., & Fathi, M. (2025) · Service Industries Journal · doi:10.1080/02642069.2025.2485983 · Cited by 11
“In the context of generative AI, trust increases users’ confidence in the tool’s abilities and enhances their overall experience (Shahzad et al., 2025).”
A bibliometric analysis of the economic effects of using artificial intelligence and ChatGPT tools in higher education institutions
Воронцова, А., Тарасенко, С., Duranowski, W., Durasiewicz, A., Soss, J., & Біловол, А. (2025) · Problems and Perspectives in Management · doi:10.21511/ppm.23(1).2025.08 · Cited by 12
Enhancing teachers’ job satisfaction through the artificial intelligence utilization
Bhojak, N. P., Momin, M. K., Jani, D., & Mathur, A. (2025) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-03-2024-0126 · Cited by 15
Investigating the use of AI tools in English language learning: A phenomenological approach
Wu, X., & Annamalai, N. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16188 · Cited by 25
The double-edged sword of technology: Investigating technostress and techno-eustress in academic burnout through digital literacy, internet self-efficacy, and cognitive flexibility
Avcı, M. Z. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105540 · Cited by 12
The impact of AI tools enabled student engagement on academic performance, employability, and empowerment in Ha'il Region, Saudi Arabia
Singh, A., Singh, H. P., & Alam, M. F. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.106103 · Cited by 20
Understanding perceived learning and the role of digital self-efficacy in generative AI adoption among STEM and non-STEM students
Paredes-Aguirre, M., Bolaños-Mendoza, C., Ortíz-Rojas, M., Martinez-Tovar, J., Sanchez-Mendoza, M., & Moreno-Abramowicz, M. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2614093 · Cited by 12
A Critical Review of Privacy, Bias, and Trust Challenges in Generative AI for Educational Contexts
Kumar, G., & Vavekanand, R. (2025) · IEEE International Conference on Data and Software Engineering (ICoDSE) · doi:10.1109/icodse68111.2025.11351807 · Cited by 10
“Proposed remedies include fairness audits, diverse and synthetic datasets, and explainable interfaces [4], [6], [20].”
Influential
Determinants of Artificial Intelligence Addiction: Anxiety, Self-Efficacy, Attitudes, Literacy, Perceived Ease of Use, Behavioral Intention, and Perceived Usefulness
Canbeldek, M., Erol, A., Erol, M., & Budak, K. S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2619035 · Cited by 12
The effect of generative artificial intelligence literacy on academic achievement: the mediating role of academic self-efficacy and the moderating role of critical thinking
Wang, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1761562 · Cited by 16
Educators’ perceptions of generative AI: Investigating attitudes, barriers and learning needs in higher education
Soleimani, S., Farrokhnia, M., van Dijk, A., & Noroozi, O. (2025) · Innovations in Education and Teaching International · doi:10.1080/14703297.2025.2530767 · Cited by 26
Managing Artificial Intelligence Ethics in Higher Education: A Systematic Framework for Issues and Policy Recommendations
Kaşarcı, İ., Akın Demircan, Z., Çeliker Ercan, G., & İnci, T. (2025) · International Journal of Current Educational Studies · doi:10.46328/ijces.223 · Cited by 8
“Empirical studies suggest that when AI-enhanced learning environments are grounded in principles of fairness, transparency, and trust, they can positively influence learning performance while developing students' ethical sensitivity (Shahzad et al., 2025).”
Affordances of, attachment to and satisfaction with generative artificial intelligence: an affordance actualization perspective
Li, Q., Sun, Y.-Q., & Yang, B. (2025) · Information Technology & People · doi:10.1108/itp-05-2024-0667 · Cited by 9
Exploring the Drivers and Outcomes of Generative AI Usage: The Role of Metacognition
Lee, Y., Ahn, H., & Jo, H. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2526592 · Cited by 9
“This aligns with previous research, which found that high self-efficacy leads to increased usage of AI technologies, as confident users are more likely to experiment with and fully exploit the tool ’ s capabilities (Pellas, 2023; Shahzad et al., 2025).”
Understanding effects of engaging online learning environments on students’ cognitive engagement and well-being: the role of academic self-concept and flow
Shi, Y., Chen, M., Wei, Y., Shen, J., Wu, M., & Zhu, K. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1614109 · Cited by 11
From Support Tool to Learning Partner: A Systematic Review of GenAI Integration in University Science Labs
Kalogiannakis, M., Papakonstantinou, N., & Sotiropoulos, D. J. (2025) · Creative Education · doi:10.4236/ce.2025.169083 · Cited by 8
The impact of Chinese undergraduates’ perceived acceptance of AI technology on academic achievement: the mediating role of critical thinking
Liu, C., Zhu, K., Zhang, Z., & Ding, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1727037 · Cited by 12
Understanding generative artificial intelligence adoption in higher education faculty: evidence from Chinese Universities and technical and vocational colleges
Luo, Y., Zhou, G., & Cui, Y. (2026) · Education and Information Technologies · doi:10.1007/s10639-025-13885-y · Cited by 14
Research on Influencing Factors of Users’ Willingness to Adopt GAI for Collaborative Decision-Making in Generative Artificial Intelligence Context
Deng, J., Wu, F., & Qi, J. (2025) · Applied Sciences · doi:10.3390/app151910322 · Cited by 9
The double-edged sword effect of GenAI assistance on university students' academic performance: evidence from China
Ma, Y., Zhang, Z., & Liu, C. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13850-9 · Cited by 6
“GenAI assistance was measured by using 6 items questionnaire from Shahzad et al. (2025).”
AI literacy and creative self-beliefs among Chinese college students: examining the underlying mechanisms and boundary conditions
Peng, C., Zhang, Z., Zhang, J., Zhang, J., Zhang, Z., Liu, Y., Jia, R., & Xu, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1719970 · Cited by 8
When Generative Artificial Intelligence Becomes a Colleague: Dual Pathways of Empowerment and Depletion in University Design Teachers’ Work Behaviors
Ding, N., Hu, L., Kim, K.-T., & Chen, M. (2026) · Sustainability · doi:10.3390/su18041775 · Cited by 8
AI self-efficacy and knowledge graph-integrated generative AI feedback in higher education
Tu, Y.-F., Liu, G.-P., Hwang, G., Chen, X.-W., & Guo, X.-G. (2026) · The Internet and Higher Education · doi:10.1016/j.iheduc.2026.101079 · Cited by 8
The Role of Generative Artificial Intelligence in Advancing Sustainable and Environmentally Responsible Teaching Practices Among Postgraduate Students
Al-Shamrani, A. S. A., Alhomayani, R. E. S., & Ibrahim, A. M. (2026) · Sustainability · doi:10.3390/su18052450 · Cited by 9
LETTERS TO THE EDITOR IN MEDICINE SHOULD NOT BE GENERATED BY AI
da Silva, J. A. T., & Daly, T. P. (2025) · Central Asian Journal of Medical Hypotheses and Ethics · doi:10.47316/cajmhe.2025.6.4.02 · Cited by 6
Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications
Szűts, Z., Molnar, T., Racsko, R., Vaughan, G., Ceglédi, S., & Dominek, D. L. (2025) · Computers · doi:10.3390/computers14070275 · Cited by 9
Digital Learning Empowering Sustainable Education: Evidence from the Determinants of Chinese College Students’ Knowledge Innovation Capability
Huang, Y., Zhang, Z., Xu, B., Zhou, X., Zhai, J., & Gao, D. (2025) · Sustainability · doi:10.3390/su17209060 · Cited by 7
Critical thinking and AI-Assisted creativity in engineering education: differences between Undergraduate, Master’s, and doctoral students
Guo, H., Zhou, Z., Ma, F., & Ning, Z. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13844-7 · Cited by 5
Research on the application behavior of generative artificial intelligence learning of college students based on self-determination theory
Fang, X., & Feng, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1805498 · Cited by 7
“…settings, learners with higher intrinsic motivation, which often stems from greater self-efficacy, demonstrate greater attentional focus on critical information and achieve better learning performance through generative AI tools (Wang et al., 2022; Shahzad et al., 2025; Tokan and Imakulata, 2019).”
The impact of AI Literacy, perception of technology, and student engagement on academic performance in GenAI-Supported learning: a study based on FsQCA and NCA methods
Yin, X., Ye, J., Yu, S., Li, H., & Lu, G. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13831-y · Cited by 6
The impact of DeepSeek’s perceived interactivity on medical students’ self-directed learning ability
Ju, Y., Li, J., Zhang, X., Wu, M., Pang, X., Li, Z., Wang, J., Li, J., Zhang, Y., & Dai, X. (2026) · Scientific Reports · doi:10.1038/s41598-025-33780-3 · Cited by 4
From trust to dependency: how generative AI attributes and organisational support shape student use of generative AI
Wang, H., & Tian, J. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13983-5 · Cited by 4
“Existing studies have also empirically examined the effects of GenAI use, including its impact on students’ learning outcomes, learning motivation, perceived ethics, and concerns (e.g., Hakiki et al., 2023; Farhi et al., 2023; Siregar et al., 2023 ; Shahzad et al., 2025).”
Unpacking the impact mechanism of generative AI-based learning analytics technology on student learning engagement: the mediating role of community of inquiry and the moderating role of digital literacy
Dong, H., Chen, X., & Shen, J. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00591-z · Cited by 15
The influence of perceived AI capability on learning engagement in smart education environments: the mediating role of academic self-efficacy and the moderating role of teacher-student relationship
Xiang, Y., & Zhang, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1790095 · Cited by 4
“…self-efficacy as a hub in engagement mechanisms; studies in generative AI learning contexts have found links between enhanced critical thinking and self-efficacy, revealing the role of psychological process variables in shaping learning outcomes (Shahzad et al., 2025; Liang et al., 2023).”
The Perils of Perfection: Navigating the Ripple Effects of Organizational Perfectionism on Employee Misbehavior through Job Insecurity and the Buffering Role of AI Learning Self-Efficacy
Kim, B., Oh, H.-J., Kim, M., & Lee, D. (2024) · Behavioral Sciences · doi:10.3390/bs14100937 · Cited by 7
“As AI becomes increasingly prevalent in various sectors, understanding the factors that influence individuals’ confidence and motivation to engage in AI learning has gained significant importance in educational and organizational research [16,19–21 , 56 ].”
Background
Empowering Academic Performance through Generative AI: The Role of Collaborative E-Learning and Effort Expectancy
Shahzad, M. F., Xu, S., & Zahid, H. (2025) · SHS Web of Conferences · doi:10.1051/shsconf/202522301001 · Cited by 3
Self-citation
Communicating the environment through artificial intelligence: ChatGPT’s potential for raising environmental awareness
Uyanık, G. (2025) · The Journal of Educational Research · doi:10.1080/00220671.2025.2571680 · Cited by 3
Leveraging Network Analytics to Examine the Impact of Generative Artificial Intelligence-Assisted Feedback on Inquiry-Based Discussion
Ba, S., Lu, G., Mendoza, N. B., Yang, Y. P., Pan, Z., & Wang, Y. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251396357 · Cited by 3
Enhancing adolescents’ exercise motivation management with generative AI anthropomorphism: a chain-mediated model of technology acceptance and self-efficacy
Wang, K., Li, H., Yu, G., Li, G., & Song, Y. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1662331 · Cited by 3
Opportunities and Challenges of AI Adoption in Higher Education, a Post COVID-19 Analysis
Dube, K., Gwatidzo, S., & Maleho, L. M. (2025) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258206 · Cited by 3
“Although the advent of generative AI has been widely adopted, it has not been well received and adopted by everyone within the academic community [6], [7], [8], [9], [10].”
Smart Learning with Generative AI Tools in Higher Education: An Integrated SOR–SDT Model of Student Creative Confidence and Engagement
Huang, Y., Yu, T., Chen, Y., Tian, Y., & Yim, J. (2025) · Applied Sciences · doi:10.3390/app16010063 · Cited by 5
The power of beliefs: How Generative Artificial Intelligence (GenAI) influences academic performance
Zhu, W., Li, T. C., & Li, Q. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106218 · Cited by 6
Generative AI in higher education: the roles of knowledge, willingness, concerns, gamification and digital literacy in shaping perceived learning effectiveness and academic performance
Alsuwaiket, M. A. (2026) · Kybernetes · doi:10.1108/k-07-2025-1865 · Cited by 3
Generative AI for feedback and collaborative knowledge construction in preservice physics teacher education
Jiang, Y., Feng, X.-M., Liu, Y., Wang, Y., Li, X., & Bao, L. (2026) · Physical Review Physics Education Research · doi:10.1103/hm13-jv98 · Cited by 6
Bridging individual and contextual influences: exploring the multilevel pathways of generative AI in shaping academic self-efficacy
Yu, H., Fu, Y., & Zhong, S. (2026) · Current Psychology · doi:10.1007/s12144-025-08937-y · Cited by 4
“Related studies have also shown that AI-supported personalized feedback and cognitive adjustment play a positive role in enhancing self-efficacy (Shahzad et al., 2025).”
The coalescent architecture of agency: normative directionality as the key to human–AI integration
Torres–Martínez, S. (2026) · AI & Society · doi:10.1007/s00146-026-02979-x · Cited by 3
“Midtended cognition clarifies the distinctive structure of human–AI creativity but leaves unresolved issues of authorship, agency attribution (Shahzad et al. 2024; Vartiainen et al. 2025), and the conditions for authentic rather than alienated cognitive products (Gómez-Martínez et al. 2023).”
Psychometric Evaluation of A Creative Thinking Performance Test for Science Education
Hasibuan, M. P., Sunarno, W., & Vh, E. S. (2026) · F1000Research · doi:10.12688/f1000research.178138.1 · Cited by 3
Effects of Generative AI Tools On Students’ Critical Thinking: A Three-Level Meta-Analysis
Sun, X., Liu, C., Li, P., & Cai, Z. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10180-1 · Cited by 7
Using Generative AI in Learning and Students’ Innovative Behavior: A Dual-Path Examination Based on the UTAUT Model
Huang, L., & Luo, W. (2026) · Behavioral Sciences · doi:10.3390/bs16061002 · Cited by 4
“In the context of higher education, studies have explored technological tools such as GAI that improve learning outcomes, increase learning opportunities, and offer personalized learning experiences (Shahzad et al., 2025).”
Effects of Chinese College Students’ Academic Integrity Perceptions on the Intention To Disclose AI-Generated Outputs: A Moderated Mediation Model
Zhang, X., Zhang, J., & Oubibi, M. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09622-2 · Cited by 5
“As a transformative educational resource, generative AI not only enriches the interaction between teachers and students but also brings a series of positive impacts to higher education (Chan & Tsi, 2024; Shahzad et al., 2024).”
The Impact of Artificial Intelligence on English Language Learning: A Systematic Review of Tools, Methods, and Outcomes in Language Skills
Alvarado, M. V. G., & Andrade, S. S. N. (2025) · Runas Journal of Education and Culture · doi:10.46652/runas.v6i12.287 · Cited by 13
Understanding User Trust in Interactive AI Learning Environments: The Role of Social Perception and User Experience
Li, H., Liu, Y., Du, J., & Pan, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3624627 · Cited by 2
“Shahzad et al. [67] found that students’ trust in generative AI systems (e.g., ChatGPT) significantly moderates the relationship between self-efficacy and learning performance.”
Competências digitais discentes para o uso de inteligência artificial no ensino superior a distância
Pinto, A. D. S., Longhi, M. T., & Behar, P. A. (2025) · RDBCI Revista Digital de Biblioteconomia e Ciência da Informação · doi:10.20396/rdbci.v24i00.8679434 · Cited by 6
Opportunity or threat? A meta-analysis of the impact of human-AI collaboration systems on employee work effectiveness
Yixiao, S. O. N. G., Zeng, M., & Tao, S. U. (2026) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2026.0461 · Cited by 3
Seeing the spark or the storm? Exploring how AI usage shapes undergraduates’ entrepreneurial intention
Zou, Y., & Guo, Y. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101361 · Cited by 5
Exploring How to Improve Programming Learning Experience with Generative AI Tools: An Integrated Means-End Chain and fsQCA Approach
Wang, R. Y., Chen, J., Zhao, J., & Fu, H. (2026) · SAGE Open · doi:10.1177/21582440251413066 · Cited by 3
“Psychological consequences mainly reflect the learners’ satisfaction with the assistance provided by AI-assisted programming learning (Shahzad et al., 2024), and can be measured through the satisfaction variable.”
Understanding users’ self-disclosure intention in generative artificial intelligence interactions: a mixed-methods empirical study
Meng, Z., Xu, Z., Yu, X., & Shah, S. H. (2026) · Current Psychology · doi:10.1007/s12144-026-09221-3 · Cited by 2
“Full measurement scales are provided in Appendix A. Perceived trust was measured with three items (Shahzad et al., 2024) (e.g., “I believe interacting with generative artificial intelligence is safe”).”
Influential
AI-enhanced education: Exploring the impact of students’ GenAI literacy on their psychological factors and learning performance
Lin, Y., Wang, W., & Chen, C. (2026) · The International Journal of Management Education · doi:10.1016/j.ijme.2026.101411 · Cited by 3

Authors: Shahzad, M. F., Xu, S., & Zahid, H.
Affiliations: Beijing University of Technology (CN)
University of the Punjab (PK)
Corresponding author: Shahzad, Muhammad Farrukh
ORCID: Shahzad, Muhammad Farrukh
Xu, Shuo
Zahid, Hira
Venue: Education and Information Technologies
Publisher: Springer Science and Business Media LLC
Published: 2024-08-24
Volume: 30
Issue: 3
Pages: 3691-3716
ISSN: 1360-2357 (ISSN-L)
1573-7608
DOI: 10.1007/s10639-024-12949-9
OpenAlex: W4401846772
Semantic Scholar: 752710bda78d91ff3a5a9f1c9a9cc5440a605673
Cited by: 204
References: 88
Citations by year: 2026: 112 · 2025: 83 · 2024: 9
FWCI: 76.70
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Education and Information Technologies (publishedVersion)
Funders: National Natural Science Foundation of China (72074014)
Topics: Social Sciences › Education › Artificial Intelligence in Education
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords: generative AI in education, learning performance, self-efficacy, creativity, trust, large language models, ChatGPT, chatbots, social cognitive theory, university students, moderating effect, Chinese universities

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks