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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Improved AI literacy and sustainable integration can foster creativity and self-efficacy, thereby enhancing learning performance and academic achievements [6].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [6]
In the citing paper
“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).”
From Managing Artificial Intelligence Ethics in Higher Education: A Systematic Framework for Issues and Policy Recommendations (2025) · cites this paper as Shahzad et al., 2025
In the citing paper
“To ensure content validity, the questionnaire items were adapted from established scales validated by prior studies [56].”
From From Perception to Practice: Artificial Intelligence as a Pathway to Enhancing Digital Literacy in Higher Education Teaching (2025) · cites this paper as [56]
In the citing paper
“The analysis yielded consistently positive Q 2 values across all endogenous constructs, surpassing the null threshold and confirming the model's predictive relevance (Shahzad et al., 2024).”
From From Perception to Practice: Artificial Intelligence as a Pathway to Enhancing Digital Literacy in Higher Education Teaching (2025) · cites this paper as Shahzad et al., 2024
Citations
Cites
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
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
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
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
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
Cited by
Sorted by citations
Items 201–205 of 205
Khasraghi, B. J., Su, Q., & Khasraghi, H. J. (2026) · Information Technology and People · doi:10.1108/itp-10-2025-1539
Guo, H., Zhou, Z., & Ma, F. (2026) · Current Psychology · doi:10.1007/s12144-026-10177-7
“Shahzad et al. ( 2024 ) further iden - tify AI self-efficacy as a key mediating mechanism linking GAI usage to creative performance, suggesting that the association between technological engagement and cre - ativity may operate primarily through students’ stronger sense of agency.”
Empowering Green Student Behaviour
Alagele, H. K. H., Neama, N. H., & Bnilam, A. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7857-2.ch013
Li, J., Hu, Y., & Zhou, L. (2026) · International Conference on Intelligent Education and Computer Technology (IECT) · doi:10.1145/3845354.3845356
“Central to this transformation is students’ actual use of AI tools, which has emerged as a fundamental behavioral construct under-pinning engagement in AI-enhanced learning environments [1][2].”
Event-Anchored Trust in AI-Powered Assessment: Why Trust Collapses Faster than It Repairs
Zhang, H. (2026) · SAGE Open · doi:10.1177/21582440261502067
Metadata
- Authors
- Shahzad, M. F., Xu, S., & Zahid, H.
- Affiliations
- Beijing University of Technology (CN)
- University of the Punjab (PK)
- Corresponding author
- Shahzad, Muhammad Farrukh
- Venue
- Education and Information Technologies
- Published
- 2024-08-24
- Volume
- 30
- Issue
- 3
- Pages
- 3691-3716
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- 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
- 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
