Abstract
This research endeavors to introduce a comprehensive scale tailored to gauge high school students' attitudes toward AI-based interviews for university admissions. Moreover, the study delves into the impact of information provisioning on students' attitudes toward AI-based interviews. The participant cohort comprised 604 high school students, who were on average 17.93 years old(S.D. = 0.80). The structured questionnaire, containing the newly formulated items, was distributed via Google Forms. In Study 1, two distinct scales were devised: the Positive Aspects of AI-based Interview Scale(PAAIS), encompassing 14 items to gauge affirmative perceptions, and the Negative Aspects of AI-based Interview Scale(NAAIS), comprising 11 items targeting adverse perceptions linked to AI-based interviews. The outcomes of exploratory factor analysis unveiled three sub-constructs composing PAAIS labeled as ‘comfortable,’ ‘convenient,’ and ‘fair,’ while NAAIS exhibited dimensions of ‘distrustful’ and ‘unpleasant.’ Subsequently, in Study 2, it was discerned that students’ attitudes towards AI-based interviews could be influenced through exposure to pertinent information concerning advanced AI technology or the underlying rationale behind such interviews. Notably, the absence of attitude alteration with respect to the ‘societal trend’ element underscores the perception that the principles governing recruitment and selection within corporate domains may not be seamlessly applicable to the university entrance examination domain due to differing contextual considerations. The paper includes a discourse encompassing reflections, implications, and constraints linked to the adoption of AI-based interviews in higher education institutions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
Data will be made available on reasonable request.
References
Acikgoz, Y., Davison, K. H., Compagnone, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28(4), 399–416. https://doi.org/10.1111/ijsa.12306
Acikkar, M., & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36(3), 7228–7233. https://doi.org/10.1016/j.eswa.2008.09.007
Alothman, B., Alazmi, H., Ali, M. B., Alqallaf, N., & Khan, M. (2022). Accelerating university admission system using machine learning techniques. In 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, pp. 439–443. https://doi.org/10.1109/ICUFN55119.2022.9829611
Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35, 611–623. https://doi.org/10.1007/s00146-019-00931-w
Assiri, B., Bashraheel, M., & Alsuri, A. (2022). Improve the accuracy of students admission at universities using machine learning techniques. In 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA), IEEE, pp. 127–132. https://doi.org/10.1109/CDMA54072.2022.00026
Beak, S. J., Choi, S. H., Ryu, Y. H., Kim, M. J., & Park, E. Y. (2015). Developing interview tool for selecting applicants with aptitude for their major undergraduate admission assessment. The Journal of Modern Social Science Research, 20, 1–29. https://doi.org/10.15730/hss.2015.20.1.001
BECU. (2020). Artificial intelligence: What consumers say: Findings and policy recommendations of a multi-country survey on AI. Retrieved July 18, 2021 from https://www.beuc.eu/reports/artificial-intelligence-what-consumers-say-findings-and-policy-recommendations-multi
Blacksmith, N., Willford, J. C., & Behrend, T. S. (2016). Technology in the employment interview: A meta-analysis and future research agenda. Personnel Assessment and Decisions, 2(1), 12–20. https://doi.org/10.25035/pad.2016.02.002
Brishti, J. K., & Javed, A. (2020). The viability of AI-based recruitment process: A systematic literature review (Master’s thesis). Umeå Universitet. Retrieved July 8, 2022 from https://www.diva-portal.org/smash/get/diva2:1442986/FULLTEXT01.pdf
Cave, S., & Dihal, K. (2018). Ancient dreams of intelligent machines: 3,000 years of robots. Nature, 559(7715), 473–475. https://doi.org/10.1038/d41586-018-05773-y
Dinello, D. (2005). Technophobia: Science fiction visions of posthuman technology (1st ed.). Austin: University of Texas Press. Retrieved August 22, 2022, from https://kwarc.info/teaching/TDM/Dinello.pdf
Evgeniou, T., Hardoon, D. R., & Ovchinnikov, A. (2020). What happens when AI is used to set grades? Harvard Business Review. Retrieved December 01, 2023, from https://hbr.org/2020/08/what-happens-when-ai-is-used-to-set-grades
Fernández-Martínez, C., & Fernández, A. (2020). AI and recruiting software: Ethical and legal implications. Paladyn, Journal of Behavioral Robotics, 11(1), 199–216. https://doi.org/10.1515/pjbr-2020-0030
Gbenga, O., Okedigba, T., & Oluwatobi, H. (2020). An improved rapid response model for university admission enquiry system using chatbot. Int. J. Comput, 38(1), 123–131. Retrieved August 08, 2022, from https://www.researchgate.net/publication/342248071
Grigorieff, A., Roth, C., & Ubfal, D. (2020). Does information change attitudes toward immigrants? Demography, 57(3), 1117–1143. https://doi.org/10.1007/s13524-020-00882-8
Gyeongbok University. (2020). AI Interview Guide. Retrieved July 12, 2021, from https://efms.kbu.ac.kr/efms/CMS/Board/Board.do?mCode=MN053&page=21&mode=view&mgr_seq=41&board_seq=2949
Hefny, E. W., Mansy, Y., Abdallah, M., & Abdennadher, S. (2021). Jooka: A bilingual chatbot for university admission. In Á. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & A. M. Ramalho Correia (Eds.), Trends and applications in information systems and technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, 1367. https://doi.org/10.1007/978-3-030-72660-7_64
Ibrahim, W. M. R. W., & Hassan, R. (2019). Recruitment trends in the era of industry 4.0 using artificial intelligence: pro and cons. Asian Journal of Research in Business and Management, 1(1), 16–21. Retrieved July 12, 2022, from http://myjms.mohe.gov.my/index.php/ajrbm
Jayne, M. (2020, August 17). Exam results: NI education minister in U-turn on A-level grades. BBC News. Retrieved March 18, 2021, from https://www.bbc.com/news/uk-northern-ireland-53808428
Jeong, M. K., & Kim, B. J. (2018). An analysis of the elements of University admission system suitable to evaluation for the 2015 revised second school curriculum. The Journal of Politics of Education, 25(3), 159–184. Retrieved from http://www-riss-kr.sproxy.dongguk.edu/link?id=A105608033
Katti, J., Agarwal, J., Bharata, S., Shinde, S., Mane, S., & Biradar, V. (2022). University Admission Prediction Using Google Vertex AI. In 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR), IEEE, pp. 1–5. https://doi.org/10.1109/ICAITPR51569.2022.9844176
Keir, G., Hu, W., Filippi, C. G., Ellenbogen, L., & Woldenberg, R. (2023). Using artificial intelligence in medical school admissions screening to decrease inter-and intra-observer variability. JAMIA Open, 6(1), ooad011. https://doi.org/10.1093/jamiaopen/ooad011
Keller, B., Baleis, J., Starke, C., & Marcinkowski, F. (2019). Machine learning and artificial intelligence in higher education: a state-of-the-art report on the German University landscape. Heinrich-Heine-Universität Düsseldorf, 1–31. Retrieved June 11, 2022 https://www.hhu.de/suche/?query=Education%253A+A+State-of-the-Art+Report+on+the+German+University+Landscape
Kim, J. (2019). Fear of artificial intelligence on people’s attitudinal & behavioral attributes: An exploratory analysis of AI Phobia. GSJ, 7, 10.
Kim, J., & Bang, S. (2020). Analysis of AI interview data using unified non-crossing multiple quantile regression tree model. The Korean Journal of Applied Statistics, 33(6), 753–762. https://doi.org/10.5351/KJAS.2020.36.6.753
Kim, J. Y., & Heo, W. (2021). Artificial intelligence video interviewing for employment: perspectives from applicants, companies, developer and academicians. Information Technology & People, 35(3), 861–878. Retrieved from https://www.emerald.com/insight/0959-3845.htm
Kim, T. S., & Han, S. H. (2020, November 14). AI Interviewer Who Can't Know What's Inside... be more difficult than human. dongA.com. Retrieved March 18, 2021, from https://www.donga.com/news/Economy/article/all/20201114/103954586/1
Kimberlin, C. L., & Winterstein, A. G. (2008). Validity and reliability of measurement instruments used in research. American Journal of Health-System Pharmacy, 65(23), 2276–2284. https://doi.org/10.2146/ajhp070364
Köchling, A., Wehner, M. C., & Warkocz, J. (2022). Can I show my skills? Affective responses to artificial intelligence in the recruitment process. Review of Managerial Science, 1–30. https://doi.org/10.1007/s11846-021-00514-4
Koechling, A., Wehner, M. C., & Warkocz, J. (2023). Can I show my skills? Affective responses to artificial intelligence in the recruitment process. Review of Managerial Science, 17(6), 2109–2138. https://doi.org/10.1007/s11846-021-00462-9
Langer, M., König, C. J., & Krause, K. (2017). Examining digital interviews for personnel selection: Applicant reactions and interviewer ratings. International Journal of Selection and Assessment, 25(4), 371–382. https://doi.org/10.1111/ijsa
Lee, S. (2018). Relationship among education fever, the college-admission policy, and shadow education in South Korea. KEDI Journal of Educational Policy, 15(2), 121–138. Retrieved from https://eds-p-ebscohost-com.sproxy.dongguk.edu/eds/pdfviewer/pdfviewer?vid=0&sid=b94ada75-8915-4af8-82f2-8a049236e932%40redis
Lisa, A. K., & Talla Simo, V. R. (2021). An in-depth study on the stages of AI in recruitment process of HRM and attitudes of recruiters and recruitees towards AI in Sweden. Independent thesis advanced level, degree of master, student thesis. Umea University, Sweden. Retrieved July 25, 2023, from http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184521
Lozano, I. A., Molina, J. M., & Gijón, C. (2021). Perception of artificial intelligence in Spain. Telematics and Informatics, 63, 101672. https://doi.org/10.1016/j.tele.2021.101672
Lu, X. (2019). An empirical study on the artificial intelligence writing evaluation system in China CET. Big Data, 7(2), 121–129. https://doi.org/10.1089/big.2018.0151
Marcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020). Implications of AI (un-) fairness in higher education admissions: the effects of perceived AI (un-) fairness on exit, voice and organizational reputation. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pp. 122–130. Barcelona, Spain. https://doi.org/10.1145/3351095.3372867
McColl, R., & Michelotti, M. (2019). Sorry, could you repeat the question? Exploring video-interview recruitment practice in HRM. Human Resource Management Journal, 29(4), 637–656. https://doi.org/10.1111/1748-8583.12249
Midas HRi. (2020). Retrieved August 8, 2020, from https://www.midashri.com/aicc
Morse, L., Teodorescu, M. H. M., Awwad, Y., & Kane, G. C. (2021). Do the ends justify the means? Variation in the distributive and procedural fairness of machine learning algorithms. Journal of Business Ethics, 1–13. https://doi.org/10.1007/s10551-021-04939-5
Nomura, T., & Kanda, T. (2003). On proposing the concept of robot anxiety and considering measurement of it. In The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003. IEEE, pp. 373–378. https://doi.org/10.1109/ROMAN.2003.1251874
Nomura, T., Suzuki, T., Kanda, T., & Kato, K. (2006). Measurement of anxiety toward robots. In ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 372–377. Hatfield, UK, September 6–8, 2006. https://doi.org/10.1109/ROMAN.2006.314462
Pitman, T. (2016). Understanding ‘fairness’ in student selection: Are there differences and does it make a difference anyway? Studies in Higher Education, 41(7), 1203–1216. https://doi.org/10.1080/03075079.2014.966072
Rosen, L. D., & Maguire, P. (1990). Myths and realities of computerphobia: A meta-analysis. Anxiety Research, 3(3), 175–191. Retrieved from https://www.researchgate.net/publication/243768604
Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
Shao, L., Levine, R. A., Hyman, S., Stronach, J., & Fan, J. (2022). A Combinatorial Optimization Framework for Scoring Students in University Admissions. Evaluation Review, 46(3), 296–335. https://doi.org/10.1177/0193841X221082887
Shin, D. Y., & Jin, D. Y. (2018, December 23). Fake cover letter, AI admissions officer catches it. Seoul Economy Daily. Retrieved March 18, 2021, from https://m.sedaily.com/NewsView/1S8KTCY65F#cb
Shin, N. M., & Chang, S. J. (2021). A Study on High School Students' Perceptions of AI Interview for University Admission. The Journal of the Korea Academia-Industrial cooperation Society, 22(7), 242–251. Retrieved from https://www.researchgate.net/publication/342248071
Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., ... Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI - Künstliche Intelligenz, 35, 109–118. https://doi.org/10.1007/s13218-020-00689-0
Suen, H. Y., Chen, M. Y. C., & Lu, S. H. (2019). Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes? Computers in Human Behavior, 98, 93–101. https://doi.org/10.1016/j.chb.2019.04.012
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55.
Tussyadiah, I. P., Wang, D., Jung, T. H., & Tom Dieck, M. C. (2018). Virtual reality, presence, and attitude change: Empirical evidence from tourism. Tourism Management, 66, 140–154. https://doi.org/10.1016/j.tourman.2017.12.003
UCAS. (2016). Unconscious Bias Report 2016. Retrieved August 25, 2022, from https://www.ucas.com/file/74801/download?token=M80wi05k
Van Esch, P., Black, J. S., & Arli, D. (2021). Job candidates’ reactions to AI-enabled job application processes. AI and Ethics, 1(2), 119–130. https://doi.org/10.1007/s43681-020-00025-0
Vedapradha, R., Hariharan, R., & Shivakami, R. (2019). Artificial intelligence: A technological prototype in recruitment. Journal of Service Science and Management, 12(3), 382–390. https://doi.org/10.4236/jssm.2019.123026
Walmsley, J. (2020). Artificial intelligence and the value of transparency. AI & Society, 36(2), 585–595. https://doi.org/10.1007/s00146-020-01066-z
Woods, S. A., Ahmed, S., Nikolaou, I., Costa, A. C., & Anderson, N. R. (2020). Personnel selection in the digital age: A review of validity and applicant reactions, and future research challenges. European Journal of Work and Organizational Psychology, 29(1), 64–77. https://doi.org/10.1080/1359432X.2019.1681401
Zimdars, A. (2010). Fairness and undergraduate admission: A qualitative exploration of admissions choices at the University of Oxford. Oxford Review of Education, 36(3), 307–323. https://doi.org/10.1080/03054981003732286
Acknowledgements
The author wishes to thank all the students who participated in the survey.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors hereby declare that they have no known competing interests that are directly or indirectly related to the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shin, N., Choi, E. & Chang, S. Students’ attitudes towards an AI-based interview for university admissions: Scale development and intervention effects. Educ Inf Technol 29, 20055–20076 (2024). https://doi.org/10.1007/s10639-024-12649-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-024-12649-4