Cited by 197Artificial Intelligence in EducationTechnology Adoption and User BehaviourArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index; 1 of the 43 sources its claims cite were checked for support.

An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI

Open access
Chan, Cecilia Ka Yuk, Zhou, Wenxin·2023·Smart Learning Environments

Published version of a preprint

Abstract

This study examines the relationship between student perceptions and their intention to use generative artificial intelligence (GenAI) in higher education. With a sample of 405 students participating in the study, their knowledge, perceived value, and perceived cost of using the technology were measured by an Expectancy-Value Theory (EVT) instrument. The scales were first validated and the correlations between the different components were subsequently estimated. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of GenAI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This study validates an Expectancy-Value Theory (EVT)-based instrument for measuring higher education students’ perceptions of generative AI and examines how knowledge, perceived value, and perceived cost relate to intention to use GenAI. Among 405 Hong Kong students, perceived value showed the strongest positive correlation with intention to use, while perceived cost showed a weaker negative correlation.

Key findings

  • Perceived value was strongly positively correlated with intention to use GenAI (r = 0.606, p < 0.001); its subscales were also positively correlated: attainment value (r = 0.587), intrinsic value (r = 0.459), and utility value (r = 0.506), all p < 0.001.
  • Perceived cost was negatively correlated with intention to use (r = -0.295, p < 0.001), while knowledge (r = 0.178, p < 0.001) and past use frequency (r = 0.339, p < 0.001) were positively correlated with intention.
  • Reliability was acceptable for knowledge (Cronbach’s α = 0.812), perceived value (α = 0.876), and perceived cost (α = 0.746); CFA fit indices were reported as acceptable, with χ²/df < 3, RMSEA < 0.07, and SRMR < 0.080 for the scales.
  • Descriptive results showed limited prior use of GenAI (Q1 mean = 2.32), while intention items had means of 3.92 (Q22) and 3.86 (Q23); perceived cost item means ranged from 2.85 to 3.15.

Methods

  • Cross-sectional online survey using convenience sampling at one university in Hong Kong in March 2023; 405 valid responses out of 460, mean age 23.87 years, 51.4% male and 48.6% female, 50.9% undergraduate and 49.1% postgraduate, 52.8% STEM and 45.7% non-STEM.
  • The EVT-based instrument had 23 five-point Likert items: knowledge (Q2–Q6), perceived value (Q7–Q17; attainment Q7–Q10, intrinsic Q11–Q13, utility Q14–Q17), perceived cost (Q18–Q21), and intention to use (Q22–Q23), plus Q1 for use frequency.
  • Analyses used IBM SPSS 27 and IBM AMOS 28: descriptive statistics, Cronbach’s alpha, confirmatory factor analysis (CFA) without exploratory factor analysis, AVE/CR/HTMT validity checks, and Pearson correlations.

Limitations

  • The paper states that the sample of 405 may not fully represent the larger student population, the study was conducted at a single point in time, it focused only on higher education students, it relied on self-reported data subject to response biases, and it did not explore individual differences such as cultural background, personal experiences, and learning styles.
  • The paper also states that skipping EFA may overlook alternative factor structures or item-loading issues, though it justifies using only CFA given the strong theoretical basis.
  • Evident from the text: convenience sampling from a single Hong Kong university limits generalizability, and the cross-sectional design cannot establish causality.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/s40561-023-00284-4

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

24 Not checked yet · 24 Own result · 18 Own method · 9 Own interpretation · 3 No basis given · 1 Partly supported · accuracy not yet measured

Verify all 42 references — free account
  • It may state two separate claims as one. 30
  • Probably not a substantial claim. 25
47 of 47
  • The paper relies on this without support.

    The adaptation of GenAI in higher education is still in the exploratory stage.

    “Since the adaptation of GenAI in higher education is still in the exploratory stage, past research on AI cannot fully illustrate students' perceptions in GenAI-specific context.”
  • The paper relies on this without support.

    No currently available questionnaires measure students' perceptions of GenAI based on the EVT framework.

    “Since there are no currently available questionnaires on students' perceptions of GenAI which are based on the EVT framework, some of the questionnaire items in this study were adapted from other similar instruments.”
  • The paper relies on this without support.

    There has been limited research into the predictive utility of EVT in relation to students' attitudes and intention towards GenAI use.

    “Although the EVT has been used in many areas, there has been limited research into the predictive utility of this framework in relation to students' attitudes and intention towards GenAI use.”
  • The paper relies on this without support.

    The concept of cost is either overlooked or underdeveloped in models such as TAM and UTAUT.

    “In addition, the concept of”
  • The paper relies on this without support.

    There is a lack of robust, theoretically grounded instruments to assess students' attitudes towards GenAI adoption.

    “To date, there has been a lack of robust, theoretically grounded instruments to assess students' attitudes towards GenAI adoption, making it challenging to systematically understand the factors that influence their intention to use the technologies.”
  • The paper relies on this without support.

    There is an urgent need for research into students' acceptance and willingness to adopt GenAI.

    “To achieve this goal, students' acceptance and willingness to adopt GenAI are crucial and there is an urgent need for research into this area.”
6 of 6

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4 of 4

Citations

Cites

Show all 66 references

Research works printed but not linked: 4

  1. Chan, C. K. Y. (2023b). Is AI changing the rules of academic misconduct? An in-depth look at students' perceptions of 'AI-giarism'. (Under review).
  2. Mok, L. (2023). Hong Kong Education University approves use of chatgpt in coursework despite bans by two other schools. Hong Kong Free Press HKFP. https://hongkongfp.com/2023/03/24/hong-kong-education-university-approves-use-of-chatgpt-in-coursework-despite-bans-by-two-other-schools/
  3. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.
  4. Stüber, J. (2018). Barriers of Digital Technologies in Higher Education: A Teachers’ Perspective from a Swedish University [Mater Thesis, Linnaeus University] diva-portal. https://www.diva-portal.org/smash/get/diva2:1201871/FULLTEXT01.pdf.

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Metadata

Authors
Chan, C. K. Y., & Zhou, W.
Affiliations
University of Hong Kong (HK)
Corresponding author
Chan, Cecilia Ka Yuk
Venue
Smart Learning Environments
Published
2023-12-07
Volume
10
Issue
1
ISSN
2196-7091 (ISSN-L)
OpenAlex
W4389438662
Cited by
192
References
66
Citations by year
2026: 75 · 2025: 71 · 2024: 45 · 2023: 1
FWCI
45.45
Type
journal article
Language
en
Open access
Locations
  • Smart Learning Environments (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI, student perceptions, intention to use, expectancy-value theory, perceived value, perceived cost, technology acceptance, higher education, scale validation

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