CoChat
Sign Up
Cited by 292Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications

Open access
Al-Zahrani, Abdulrahman M., Alasmari, Talal M.·2024·Humanities and Social Sciences Communications
DOI 10.1057/s41599-024-03432-4
Open this paper in CoChat — free account

Abstract

The increasing prevalence of Artificial Intelligence (AI) in higher education underscores the necessity to explore its implications on ethical, social, and educational dynamics within the sector. This study aims to comprehensively investigate the impact of AI on higher education in Saudi Arabia, delving into stakeholders’ attitudes, perceptions, and expectations regarding its implementation. The research hones in on key facets of AI in higher education, encompassing its influence on teaching and learning, ethical and social implications, and the anticipated role of AI in the future. Employing a quantitative approach through an online survey questionnaire (N = 1113), this study reveals positive attitudes toward AI in higher education. Stakeholders recognize its potential to enhance teaching and learning, streamline administration, and foster innovation. Emphasis is placed on ethical considerations and guidelines for AI implementation, highlighting the imperative need to address issues such as privacy, security, and bias. Participants envision a future characterized by personalized learning experiences, ethically integrated AI, collaboration, and ongoing support for lifelong learning. Furthermore, the results illuminate the intricate interplay between AI usage, purposes, difficulties, and their impact on attitudes, perceptions, and future implications. Accordingly, the research underscores the necessity for a comprehensive understanding of AI integration, considering not only its technical aspects but also the ethical, social, and educational dimensions. By acknowledging the role of AI uses, AI usage purposes, and addressing associated difficulties, educational stakeholders can work towards harnessing the benefits of AI while ensuring responsible and effective implementation in teaching and learning contexts.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1057/s41599-024-03432-4

Cites

15 of 15 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • There may be a disconnect between institutional policies and students' use of AI because institutions lack clear guidance on how to appropriately employ these tools.

    “Ultimately, there may be a disconnect between institutional policies and students' use of AI as institutions lack clear guidance of how to appropriately employ these tools.”

    From Procedure to Evaluate Artificial Intelligence (AI) Output for Educational and Clinical Tasks (2026) · cites this paper as Al-Zahrani and Alasmari (2024)

    The Discussion reports participants’ AI use and recommends developing ethical guidelines for AI integration. It does not establish a disconnect between institutional policies and students’ use, or say that institutions lack clear guidance as the cause of such a disconnect. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Technological proficiency was assessed through a validated self-report instrument adapted from Thompson et al., with acceptable internal consistency (Cronbach's α = 0.89).

    “Technological proficiency was assessed through a validated self-report instrument adapted from Thompson et al.19, with acceptable internal consistency (Cronbach’s α = 0.89).”

    From Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes (2025) · cites this paper as [19]

    The Research design passage (labeled Introduction; page unavailable) says the questionnaire collected participants’ subjective AI expertise, and the Results report self-rated proficiency. But the cited paper describes its questionnaire as developed from a literature review, not adapted from Thompson et al.; its reported overall Cronbach’s α is 0.96, not 0.89. The claimed instrument and reliability figure are therefore unsupported. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Disciplinary epistemological traditions strongly predict AIGC adoption patterns.

    “Liu et al. found that disciplinary epistemological traditions strongly predict AIGC adoption patterns19.”

    From Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes (2025) · cites this paper as [19]

    The Introduction says the study included participants from diverse disciplines, and the Results report fields of study and overall AI-use frequency. Neither passage links disciplinary epistemological traditions to AIGC adoption patterns. Searches for the claimed phrase, epistemology, disciplinary differences, and adoption patterns found no such finding in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Fields with established quantitative methodologies more readily incorporate algorithmic assistance, while disciplines emphasizing interpretation and critical thinking demonstrate more selective implementation focused on augmenting rather than automating cognitive processes.

    “Fields with established quantitative methodologies more readily incorporate algorithmic assistance, while disciplines emphasizing interpretation and critical thinking demonstrate more selective implementation focused on augmenting rather than automating cognitive processes.”

    From Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes (2025) · cites this paper as [19]

    Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 44

  1. Almaiah MA, Alfaisal R, Salloum SA, Hajjej F, Thabit S, El-Qirem FA, Al-Maroof RS (2022) Examining the Impact of Artificial Intelligence and Social and Computer Anxiety in E-Learning Settings: Students’ Perceptions at the University Level. Electronics 11(22):3662, https://www.mdpi.com/2079-9292/11/22/3662
  2. Al-Zahrani AM (2024a) From Traditionalism to Algorithms: Embracing Artificial Intelligence for Effective University Teaching and Learning. Educational Technology at IgMin 2(2):102–0112. https://doi.org/10.61927/igmin151 · DOI
  3. André Renz GV (2021) Reinvigorating the Discourse on Human-Centered Artificial Intelligence in Educational Technologies. Technology Innovation Management Review 11(5):5–16. https://doi.org/10.22215/timreview/1438 · DOI
  4. Bates T, Cobo C, Mariño O, Wheeler S (2020) Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education 17(1):42. https://doi.org/10.1186/s41239-020-00218-x · DOI
  5. Bearman M, Ryan J, Ajjawi R (2022) Discourses of artificial intelligence in higher education: a critical literature review. Higher Education. https://doi.org/10.1007/s10734-022-00937-2 · DOI · Paper page
  6. Bigman YE, Wilson D, Arnestad MN, Waytz A, Gray K (2022) Algorithmic discrimination causes less moral outrage than human discrimination. Journal of Experimental Psychology 152(1):4–27. https://doi.org/10.1037/xge0001250 · DOI · Paper page
  7. Bozkurt A, Karadeniz A, Baneres D, Guerrero-Roldán AE, Rodríguez ME (2021) Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century. Sustainability 13(2):800, https://www.mdpi.com/2071-1050/13/2/800
  8. Cambridge University Press & Assessment (CUPA) (2023) The Cambridge approach to generative AI and assessment. https://www.cambridge.org/news-and-insights/news/The-Cambridge-approach-to-generative-AI-and-assessment
  9. Carmody J, Shringarpure S, Van De Venter G (2021) AI and privacy concerns: a smart meter case study. Journal of Information, Communication and Ethics in Society 19(4):492–505. https://doi.org/10.1108/jices-04-2021-0042 · DOI
  10. Chu HC, Hwang GH, Tu YF, Yang KH (2022) Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology 38(3):22–42
  11. Creely E (2022) Conceiving Creativity and Learning in a World of Artificial Intelligence: A Thinking Model. In D Henriksen & P Mishra (Eds.), Creative Provocations: Speculations on the Future of Creativity, Technology & Learning (pp. 35-50). Springer International Publishing. https://doi.org/10.1007/978-3-031-14549-0_3 · DOI
  12. Dai C-P, Ke F (2022) Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence 3:100087. https://doi.org/10.1016/j.caeai.2022.100087 · DOI
  13. Doncieux S, Chatila R, Straube S, Kirchner F (2022) Human-centered AI and robotics. AI Perspectives 4(1):1. https://doi.org/10.1186/s42467-021-00014-x · DOI
  14. Dong X, Hu J (2019) An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches. International Journal of English Linguistics, 9(5). https://doi.org/10.5539/ijel.v9n5p52 · DOI
  15. El-Ansari M (2021) Exploring the Applicability of Artificial Intelligence in Transnational Higher Education. International Journal of Management Cases 23-33(2):20, https://www.circleinternational.co.uk/wp-content/uploads/2021/07/IJMC-23.2.pdf
  16. Elliott D, Soifer E (2022) AI Technologies, Privacy, and Security. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.826737 · DOI
  17. Esparza GG, Canul-Reich J, Zezzatti A, Margain L, Ponce J (2018) Mining: Students Comments about Teacher Performance Assessment using Machine Learning Algorithms. 9(3), 26-40. http://cathi.uacj.mx/20.500.11961/5676
  18. Farisco M, Evers K, Salles A (2020) Towards Establishing Criteria for the Ethical Analysis of Artificial Intelligence. Science and Engineering Ethics 26(5):2413–2425. https://doi.org/10.1007/s11948-020-00238-w · DOI
  19. Hassan R, Ali A, Howe CW, Zin AM (2022) Constructive alignment by implementing design thinking approach in artificial intelligence course: Learners’ experience. AIP Conference Proceedings, 2433(1). https://doi.org/10.1063/5.0072986 · DOI
  20. Hooda M, Rana C, Dahiya O, Rizwan A, Hossain MS (2022) Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education. Mathematical Problems in Engineering 2022:5215722. https://doi.org/10.1155/2022/5215722 · DOI · Paper page
  21. Hu F, Qiu L, Wei S, Zhou H, Bathuure IA, Hu H (2023) The spatiotemporal evolution of global innovation networks and the changing position of China: a social network analysis based on cooperative patents. R and D Management. https://doi.org/10.1111/radm.12662 · DOI
  22. Huang S-P (2018) Effects of Using Artificial Intelligence Teaching System for Environmental Education on Environmental Knowledge and Attitude. Eurasia Journal of Mathematics, Science and Technology Education 14(7):3277–3284. https://doi.org/10.29333/ejmste/91248 · DOI · Paper page
  23. Jiang C, Pang Y (2023) Enhancing design thinking in engineering students with project-based learning. Computer Applications in Engineering Education, n/a(n/a). https://doi.org/10.1002/cae.22608 · DOI
  24. Johnson GM (2021) Algorithmic bias: on the implicit biases of social technology. Synthese 198(10):9941–9961. https://doi.org/10.1007/s11229-020-02696-y · DOI
  25. Kerr A, Barry M, Kelleher JC (2020) Expectations of artificial intelligence and the performativity of ethics: Implications for communication governance. Big Data & Society 7(1):205395172091593. https://doi.org/10.1177/2053951720915939 · DOI
  26. Kochmar E, Vu DD, Belfer R, Gupta V, Serban IV, Pineau J (2022) Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education 32(2):323–349. https://doi.org/10.1007/s40593-021-00267-x · DOI · Paper page
  27. Kordzadeh N, Ghasemaghaei M (2021) Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems 31(3):388–409. https://doi.org/10.1080/0960085x.2021.1927212 · DOI · Paper page
  28. Kuo J, Song X, Chen C, Patel CD (2021) Fostering Design Thinking in Transdisciplinary Engineering Education. In Advances in transdisciplinary engineering. IOS Press. https://doi.org/10.3233/atde210083 · DOI
  29. Li X, Ouyang F, Chen W (2022) Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions. Journal of Computing in Higher Education 34(3):790–819. https://doi.org/10.1007/s12528-022-09321-6 · DOI
  30. Liu X, Wang S, Lu S, Yin Z, Li X, Yin L, Tian J, Zheng W (2023) Adapting feature selection algorithms for the classification of Chinese texts. Systems 11(9):483. https://doi.org/10.3390/systems11090483 · DOI
  31. Liu Z, Kong X, Liu S, Yang Z (2023) Effects of computer-based mind mapping on students’ reflection, cognitive presence, and learning outcomes in an online course. Distance Education 44(3):544–562. https://doi.org/10.1080/01587919.2023.2226615 · DOI
  32. Liu Z, Wen C, Su Z, Liu S, Sun J, Kong W, Yang Z (2024) Emotion-semantic-aware dual contrastive learning for epistemic emotion identification of learner-generated reviews in MOOCs. IEEE Transactions on Neural Networks and Learning Systems, 1–14. https://doi.org/10.1109/tnnls.2023.3294636 · DOI
  33. Lockwood J (2014) Handbook of Automated Essay Evaluation Current Applications and New Directions Mark D. Shermis and Jill Burstein (eds.) (2013) New York: Routledge. pp. 194 ISBN: 9780415810968. Writing & Pedagogy 6(2):437–441. https://doi.org/10.1558/wap.v6i2.437 · DOI
  34. Ouyang F, Xu W, Cukurova M (2023) An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective. International Journal of Computer-Supported Collaborative Learning 18(1):39–66. https://doi.org/10.1007/s11412-023-09387-z · DOI · Paper page
  35. Popenici SAD, Kerr S (2017) Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning 12(1):22. https://doi.org/10.1186/s41039-017-0062-8 · DOI · Paper page
  36. Resseguier A, Rodrigues R (2020) AI ethics should not remain toothless! A call to bring back the teeth of ethics. Big Data & Society 7(2):205395172094254. https://doi.org/10.1177/2053951720942541 · DOI
  37. Stahl BC, Antoniou J, Ryan M, Macnish K, Jiya T (2021) Organisational responses to the ethical issues of artificial intelligence. AI & SOCIETY 37(1):23–37. https://doi.org/10.1007/s00146-021-01148-6 · DOI
  38. Susilawati E, Lubis H, Kesuma S, Pratama I (2022) Antecedents of Student Character in Higher Education: The role of the Automated Short Essay Scoring (ASES) digital technology-based assessment model. Eurasian Journal of Educational Research 98:203–220. https://doi.org/10.14689/ejer.2022.98.013 · DOI
  39. UNESCO (2021) Artificial Intelligence and Education. Guidance for Policy-makers. The United Nations Educational, Scientific and Cultural Organization, 1-50. https://doi.org/10.54675/PCSP7350 · DOI · Paper page
  40. Vendraminelli L, Macchion L, Nosella A, Vinelli A (2022). Design thinking: strategy for digital transformation. Journal of Business Strategy, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JBS-01-2022-0009 · DOI
  41. Wang C, Wang K, Bian AA, Islam MR, Keya KN, Foulds JR, Pan S (2022). Do Humans Prefer Debiased AI Algorithms? A Case Study in Career Recommendation. https://doi.org/10.1145/3490099.3511108 · DOI
  42. Zhang K, Aslan AB (2021) AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence 2:100025. https://doi.org/10.1016/j.caeai.2021.100025 · DOI · Paper page
  43. Zhao X, Yang M, Qu Q, Xu R, Li J (2022) Exploring privileged features for relation extraction with contrastive student-teacher learning. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/tkde.2022.3161584 · DOI
  44. Zheng W, Lu S, Cai Z, Wang R, Wang L, Yin L (2023) PAL-BERT: An Improved Question Answering Model. Computer Modeling in Engineering & Sciences, 1-17. https://doi.org/10.32604/cmes.2023.046692 · DOI

Cited by

Sorted by citations

Authors
Al-Zahrani, A. M., & Alasmari, T. M.
Affiliations
University of Jeddah (SA)
Corresponding author
Al-Zahrani, Abdulrahman M.
ORCID
Venue
Humanities and Social Sciences Communications
Publisher
Springer Science and Business Media LLC
Published
2024-07-13
Volume
11
Issue
1
ISSN
  • 2055-1045 (ISSN-L)
  • 2662-9992
DOI
10.1057/s41599-024-03432-4
OpenAlex
W4400609581
Cited by
292
References
15
Citations by year
2026: 141 · 2025: 135 · 2024: 16
FWCI
109.79
Type
journal article
Language
en
Open access
Licence
Locations
  • Humanities and Social Sciences Communications (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence in higher education, AI ethics, AI in teaching and learning, privacy and security, algorithmic bias, personalized learning, lifelong learning

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