CoChat
Sign Up
Cited by 18Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Awareness, benefits, threats, attitudes, and satisfaction with AI tools among Asian and African higher education staff and students

Open access
Ahmad, Muayyad, Subih, Maha, Fawaz, Mirna, Alnuqaidan, Hanan, Abuejheisheh, Ashraf, Naqshbandi, Vian, Alhalaiqa, Fadwa·2024·Journal of Applied Learning and Teaching
DOI 10.37074/jalt.2024.7.1.10
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) tools are now used in our daily lives. This study aimed to explore the level of awareness, perceived benefits, threats, attitudes, and level of satisfaction with AI tools among individuals within higher education in Asia and Africa. A cross-sectional study was conducted in August 2023. Snowball sampling was used with a convenience sample of 815 highly educated Asian and African participants from 11 countries. About 56% of participants have Bachelor’s degrees. 312 participants (38%) were unaware of AI tools and AI tools were used rarely by 316 (63%) of 503 participants who were aware of them. ChatGPT is the most popular of this study’s AI tools (N=405, 81%). Participants who used AI tools reported greater benefits than those who did not (p < 0.05). ¬Of the four educational groups, those with a Master’s degree reported a higher AI tool threat than those with a Diploma (P < 0.05). Female participants reported more AI-related threats than males (P < 0.05). In conclusion, this research is important because of the rapid development of modern technology around the world. Nevertheless, Asia and Africa still lag behind developed nations in AI technology awareness.

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 · 10.37074/jalt.2024.7.1.10

Cites

12 of 12 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?

  • Technology awareness is positively correlated with the use of GenAI tools.

    “Studies found that technology awareness was positively correlated with the use of GenAI tools (Ahmad et al., 2024; Gaber et al., 2023; Hasan et al., 2024)”

    From Planets, cars, and personas: Toward an understanding of faculty perceptions of artificial intelligence through visual metaphors and projective techniques (2025) · cites this paper as Ahmad et al., 2024

    The Results (page unavailable) report awareness and use separately: 38% of participants were unaware of AI tools, and 63% of those aware had rarely used them. Neither the Results nor the Discussion passages found by searching for an awareness–use correlation report a positive correlation between technology awareness and GenAI-tool use. (LLM, conf=0.77)

    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: 33

  1. Abdullah, K. H., & Sofyan, D. (2023). Machine learning in safety and health research: A scientometric analysis. International Journal of Information Science and Management (IJISM), 21(1), 17-37. https://doi.org/10.22034/ijism.2022.1977763. · DOI
  2. Adarkwah, M. A., Amponsah, S., van Wyk, M. M., Huang, R., Tlili, A., Shehata, B., Metwally, A. H. S., & Wang, H. (2023). Awareness and acceptance of ChatGPT as a generative conversational AI for transforming education by Ghanaian academics: A two-phase study. Journal of Applied Learning and Teaching, 6(2), 78-93. https://doi.org/10.37074/ jalt.2023.6.2.26 · DOI
  3. Ahmad, M., Alhalaiqa, F., & Subih, M. (2023). Constructing and testing the psychometrics of an instrument to measure the attitudes, benefits, and threats associated with the use of Artificial Intelligence tools in higher education. Journal of Applied Learning and Teaching, 6(2), 114-120. https://doi. org/10.37074/jalt.2023.6.2.36 · DOI
  4. Aitkazinov, A. (2023). The role of artificial intelligence in auditing: Opportunities and challenges. International Journal of Research in Engineering, Science and Management, 6(6), 117-119. https://doi.org/10.7176/RJFA/14-15-03 · DOI
  5. Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), 100333. https://doi. org/10.1016/j.jik.2023.100333 · DOI · Paper page
  6. Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., & Al Yami, M. S. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016 · DOI · Paper page
  7. Ashcroft, R. E. (2008). The declaration of Helsinki. The Oxford textbook of clinical research ethics, 141-148.
  8. Atenas, J., Havemann, L., & Timmermann, C. (2023). Reframing data ethics in research methods education: a pathway to critical data literacy. International Journal of Educational Technology in Higher Education, 20(1), 1-27. https://doi.org/10.1186/s41239-023-00380-y · DOI
  9. Bærøe, K., & Gundersen, T. (2023). Trustworthy bioethicists within lifecycles of artificial intelligence in health. In AI and society (pp. 249-264).
  10. Bani Hani, S. H., & Ahmad, M. M. (2023). Machine-learning algorithms for ischemic heart disease prediction: A systematic review. Current Cardiology Reviews, 19(1), 87-99. https://doi.org/10.2174/1573403X18666220609123053 · DOI
  11. Basilotta-Gómez-Pablos, V., Matarranz, M., Casado-Aranda, L.-A., & Otto, A. (2022). Teachers' digital competencies in higher education: a systematic literature review. International Journal of Educational Technology in Higher Education, 19(1), 1-16. https://doi.org/10.1186/s41239-021-00312-8 · DOI
  12. Benvenuti, M., Cangelosi, A., Weinberger, A., Mazzoni, E., Benassi, M., Barbaresi, M., & Orsoni, M. (2023). Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context. Computers in Human Behavior, 107903. https://doi.org/10.1016/j.chb.2023.107903 · DOI · Paper page
  13. Bharadiya, J. P. (2023). A comparative study of business intelligence and artificial intelligence with big data analytics. American Journal of Artificial Intelligence, 7(1), 24. https:// doi.org/10.11648/j.ajai.20230701. · DOI
  14. Byrom, B., Elash, C. A., Eremenco, S., Bodart, S., Muehlhausen, W., Platko, J. V., Watson, C., & Howry, C. (2022). Measurement comparability of electronic and paper administration of visual analogue scales: A review of published studies. Therapeutic Innovation & Regulatory Science, 56(3), 394-404. https://doi.org/10.1007/s43441-022-00376-2 · DOI
  15. Couture, V., Roy, M., Dez, E., Laperle, S., & Bélisle-Pipon, J. (2023). Ethical implications of artificial intelligence in population health and the public's role in its governance: perspectives from a citizen and expert panel. Journal of Medical Internet Research, 25, e44357. https://doi. org/10.2196/44357 · DOI
  16. Dergaa, I., Chamari, K., Zmijewski, P., & Saad, H. B. (2023). From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biology of Sport, 40(2), 615-622. https:// doi.org/10.5114/biolsport.2023.125623 · DOI · Paper page
  17. Himeur, Y., Elnour, M., Fadli, F., Meskin, N., Petri, I., Rezgui, Y., Bensaali, F., & Amira, A. (2023). AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives. Artificial Intelligence Review, 56(6), 4929-5021. https://doi. org/10.1007/s10462-022-10286-2 · DOI
  18. Hu, K., Lou, B., & Baloch, B. (2023). Reducing human bias in decision making and improving customer satisfaction through artificial intelligence: Empirical evidence from a knowledge service company. SSRN 4436554.
  19. Ifelebuegu, A. O. (2023). Rethinking online assessment strategies: Authenticity versus AI chatbot intervention. Journal of Applied Learning and Teaching, 6(2), 385-392. https://doi.org/10.37074/jalt.2023.6.2.2 · DOI
  20. Ifelebuegu, A. O., Kulume, P., & Cherukut, P. (2023). Chatbots and AI in Education (AIEd) tools: The good, the bad, and the ugly. Journal of Applied Learning and Teaching, 6(2), 332- 345. https://doi.org/10.37074/jalt.2023.6.2.29 · DOI
  21. Ismail, F., Tan, E., Rudolph, J., Crawford, J., & Tan, S. (2023). Artificial intelligence in higher education. A protocol paper for a systematic literature review. Journal of Applied Learning and Teaching, 6(2), 56-63. https://doi.org/10.37074/ jalt.2023.6.2.34 · DOI
  22. Kukulska-Hulme, A., Giri, R. A., Dawadi, S., Devkota, K. R., & Gaved, M. (2023). Languages and technologies in education at school and outside of school: Perspectives from young people in low-resource countries in Africa and Asia. Frontiers in Communication, 8, 1081155. https://doi.org/10.3389/ fcomm.2023.1081155 · DOI
  23. Liang, M., Lim, C. P., Park, J., & Mendoza, N. B. (2023). A review of ICT-enabled learning for schoolgirls in Asia and its impacts on education equity. Educational technology research and development, 71(2), 267-293. https://doi. org/10.1007/s11423-022-10178-w · DOI
  24. Novitzky, P., Janssen, J., & Kokkeler, B. (2023). A systematic review of ethical challenges and opportunities of addressing domestic violence with AI-technologies and online tools. Heliyon, 9(6). https://doi.org/10.1016/j.heliyon.2023.e17140 · DOI
  25. Polit, D. F., & Beck, C T. (2013). Essentials of nursing research: Appraising evidence for nursing practice. Lippincott Williams & Wilkins.
  26. Rajagopal, N. K., Qureshi, N. I., Durga, S., Ramirez Asis, E. H., Huerta Soto, R. M., Gupta, S. K., & Deepak, S. (2022). Future of business culture: An artificial intelligence- driven digital framework for organization decision- making process. Complexity, 2022, 1-14. https://doi. org/10.1155/2022/7796507 · DOI
  27. Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30. https://doi. org/10.1097/01.NUMA.0000578988.56622. · DOI
  28. Rodway, P., & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 100150. https://doi.org/10.1016/j. caeai.2023.100150 · DOI · Paper page
  29. Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and open-ai models: A preliminary review. Future Internet, 15(6), 192. https://doi.org/10.3390/fi15060192 · DOI
  30. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342-363. https://doi.org/10.37074/jalt.2023.6.1.9 · DOI · Paper page
  31. Scherer, A. G., Neesham, C., Schoeneborn, D., & Scholz, M. (2023). New challenges to the enlightenment: How twenty-first-century sociotechnological systems facilitate organized immaturity and how to counteract it. Business Ethics Quarterly, 33(3), 409-439. https://doi.org/10.1017/ beq.2023. · DOI
  32. Thakur, R. (2024). Introduction to artificial intelligence and its importance in modern business management. In Leveraging AI and emotional intelligence in contemporary business organizations (pp. 133-165). IGI Global. https://doi. org/10.4018/979-8-3693-1902-4. · DOI
  33. Wong, B. K. M., Vengusamy, S., & Bastrygina, T. (2024). Healthcare digital transformation through the adoption of artificial intelligence. In Artificial Intelligence, big data, blockchain and 5G for the digital transformation of the healthcare industry (pp. 87-110). Elsevier. https://doi. org/10.1016/B978-0-443-21598-8.00014-2 · DOI

Cited by

Sorted by citations

Authors
Ahmad, M., Subih, M., Fawaz, M., Alnuqaidan, H., Abuejheisheh, A., Naqshbandi, V., & Alhalaiqa, F.
Affiliations
  • University of Jordan (JO)
  • Al-Zaytoonah University of Jordan (JO)
  • Beirut Arab University (LB)
  • Al-Quds University (PS)
  • Hawler Medical University (IQ)
ORCID
Venue
Journal of Applied Learning and Teaching
Publisher
Open Access Publishing Association
Published
2024-01-22
Volume
7
Issue
1
Pages
57-64
ISSN
2591-801X (ISSN-L)
DOI
10.37074/jalt.2024.7.1.10
OpenAlex
W4391095239
Cited by
18
References
12
Citations by year
2026: 4 · 2025: 9 · 2024: 5
FWCI
6.78
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Applied Learning & Teaching (publishedVersion, cc-by)
  • Qatar University QSpace (Qatar University) (submittedVersion)
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
AI tools, AI awareness, AI risks, attitudes toward AI, user satisfaction, ChatGPT, educational level differences, gender differences, Asia, Africa

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