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Students’ voices on generative AI: perceptions, benefits, and challenges in higher education

Cecilia Ka Yuk ChanWenjie Hu

article2023International Journal of Educational Technology in Higher Education2,188 citations

Analyzes empirical survey data from 399 university students to identify their actual uses, perceived learning benefits, and ethical concerns regarding generative AI, providing clear evidence for educators designing institutional integration policies.

generative aipersonalized learningprivacyStudentsGenAITechnologies
arXiv: 2305.00290
  • Paper: Training language models to follow instructions with human feedback, Long Ouyang et al. (2022). Provides the foundational technical methodology for instruction-aligned models like ChatGPT, illustrating the conversational and helpfulness characteristics that university students directly interact with and evaluate.
  • Paper: Language Models are Few-Shot Learners, T. B. Brown et al. (2020). Introduces the few-shot learning and natural language generation capabilities of modern large language models that underpin the generative AI tools analyzed in the student perception study.
  • Paper: Sparks of Artificial General Intelligence: Early experiments with GPT-4, Sébastien Bubeck et al. (2023). Explores the broader multimodal, reasoning, and domain-specific capabilities and limitations of GPT-4, providing technical depth to the academic benefits and accuracy concerns voiced by students.
  • Paper: GPT-4 Technical Report, OpenAI (2023). Details the performance benchmarks and safety evaluations of GPT-4 across academic exams, offering concrete data directly relevant to students' perceptions of academic support and ethical risks.
Cover for Students’ voices on generative AI: perceptions, benefits, and challenges in higher education

Abstract

This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs' 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students' perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students' perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education.

Topics

generative aipersonalized learningprivacyuniversityeducation technologyteaching and learninghigher education institutionsstudent learningonline learning

Keywords

StudentsGenAITechnologiesEducationHigher EducationLearningGenerativeGenAI TechnologiesPerceptionsChatGPT

Table of Contents

  • Methodology
  • Results
  • Demographic information
  • Knowledge of generative AI technologies
  • Willingness to use generative AI technologies
  • Concerns about generative AI technologies
  • The benefits and challenges for students' willingness and concerns
  • What are the reasons behind students' willingness to utilise generative AI technologies?
  • What are the reasons behind students' concerns or lack of concerns regarding generative AI technologies?
  • Discussion
  • Student Perception of GenAI Technologies
  • Conclusion
  • Implications
  • Limitations and future research
  • Acknowledgements
  • References

Knowls

  1. Knowl 1 — Categorization of Student-Perceived Benefits of Generative AI in Higher Education

    empirical result

    Thematic analysis of qualitative and quantitative responses from university students identifies five major benefit categories for generative artificial intelligence (GenAI) tools (such as ChatGPT):

    1. Personalized and Immediate Learning Support: GenAI functions as a 24/7 on-demand tutor, providing tailored explanations, immediate answers to assignment questions, customized learning materials (such as tailored second-language vocabulary exercises), and instant formative feedback to alleviate instructor workload.
    2. Writing and Brainstorming Support: GenAI assists students in overcoming writer's block, generating initial conceptual frameworks, retrieving citations, formatting text, and refining English grammar and phrasing—serving as an equalizer especially for non-native English-speaking students.
    3. Research and Analysis Support: GenAI tools assist in preliminary literature searching, synthesizing dense academic readings, generating exploratory research hypotheses, and facilitating initial data processing and analysis.
    4. Visual and Audio Multimedia Support: Text-to-image and generative visual tools (e.g., DALL-E, Stable Diffusion) aid students in visual arts, presentation slide design, and multimedia content generation.
    5. Administrative Support: GenAI assists in handling routine, non-creative, and repetitive operational tasks, allowing students and educators to allocate more time to higher-level analytical and instructional tasks.

  2. Knowl 2 — Categorization of Student-Perceived Challenges and Risks of Generative AI in Higher Education

    empirical result

    Thematic analysis of university student feedback identifies six primary dimensions of concern and challenge regarding the deployment of generative artificial intelligence (GenAI) in tertiary education:

    1. Accuracy and Transparency: Fluently presented outputs frequently risk factual inaccuracies, hallucinations, and unverified information. The opaque "black box" architecture makes decision paths unverifiable to users, reducing trust.
    2. Privacy and Ethical Concerns: Students fear unauthorized collection, retention, and misuse of private message data entered into AI systems. Furthermore, pervasive AI usage complicates academic integrity, creating issues of authorship ambiguity and undetectable plagiarism ("AI-giarism").
    3. Impairment of Holistic Competencies: Over-dependence on GenAI risks stunting students' independent critical thinking, analytical problem-solving, cognitive persistence, and creative ideation.
    4. Career Prospects and Employment Disruption: Fears of workforce displacement and automation of entry-level professional roles (e.g., junior programming, GIS analysis) coincide with anticipated increases in employer expectations and competitive skill barriers.
    5. Human Values and Educational Inequity: Potential widening of socio-economic divides between privileged users and non-users, algorithmic bias misaligned with societal norms, and deterioration of teacher-student rapport and respect.
    6. Policy Vacuum and Governance Uncertainty: The absence of institutional guidelines, clear ethical boundaries, and transparent implementation frameworks leaves students and instructors without formal guidance on acceptable tool usage.

  3. Knowl 3 — Student Knowledge of Generative AI Capabilities and Limitations

    data/table

    A 5-point Likert scale assessment (where 1=Strongly disagree1 = \text{Strongly disagree} and 5=Strongly agree5 = \text{Strongly agree}) measured Hong Kong tertiary students' (N=399N = 399) understanding of generative artificial intelligence (GenAI) systems such as ChatGPT:

    Knowledge Statement Mean SD
    I understand generative AI technologies like ChatGPT have limitations in their ability to handle complex tasks 4.15 0.82
    I understand generative AI technologies like ChatGPT can generate output that is factually inaccurate 4.10 0.85
    I understand generative AI technologies like ChatGPT can generate output that is out of context or inappropriate 4.03 0.83
    I understand generative AI technologies like ChatGPT can exhibit biases and unfairness in their output 3.93 0.92
    I understand generative AI technologies like ChatGPT may rely too heavily on statistics, which can limit their usefulness in certain contexts 3.93 0.93
    I understand generative AI technologies like ChatGPT have limited emotional intelligence and empathy, which can lead to output that is insensitive or inappropriate 3.89 0.97

    The mean scores (3.89≤Mean≤4.153.89 \le \text{Mean} \le 4.15) indicate that students generally recognize the technical constraints and risk of factual inaccuracy in GenAI outputs, but exhibit lower awareness regarding the lack of emotional intelligence and empathy. Student knowledge of GenAI is moderately positively correlated with their usage frequency (r=0.10,p<0.05r = 0.10, p < 0.05). Specifically, students who used GenAI at least sometimes exhibited significantly higher awareness that tools generate factually inaccurate output (Mean=4.22,SD=0.829\text{Mean} = 4.22, \text{SD} = 0.829) compared to students who never or rarely used them (Mean=3.99,SD=0.847\text{Mean} = 3.99, \text{SD} = 0.847; t=2.695,p<0.01t = 2.695, p < 0.01).

  4. Knowl 4 — Student Willingness and Attitudes Toward Generative AI in Higher Education

    data/table

    Evaluated on a 5-point Likert scale (1=Strongly disagree1 = \text{Strongly disagree} to 5=Strongly agree5 = \text{Strongly agree}), university students (N=399N = 399) demonstrated an overall positive willingness to integrate generative AI (GenAI) tools like ChatGPT into academic learning and future professional practices:

    Willingness Statement Mean SD
    I believe generative AI technologies such as ChatGPT can help me save time 4.20 0.82
    I think AI technologies such as ChatGPT is a great tool as it is available 24/7 4.12 0.83
    Students must learn how to use generative AI technologies well for their careers 4.05 0.96
    I envision integrating generative AI technologies like ChatGPT into my teaching and learning practices in the future 3.85 1.02
    I think AI technologies such as ChatGPT is a great tool for student support services due to anonymity 3.77 0.99
    I believe AI technologies such as ChatGPT can provide me with unique insights and perspectives that I may not have thought of myself 3.74 1.08
    I believe generative AI technologies such as ChatGPT can improve my digital competence 3.70 0.96
    I think AI technologies such as ChatGPT can provide me with personalized and immediate feedback and suggestions for my assignments 3.61 1.06

    Students' perceived willingness to adopt GenAI technologies exhibits statistically significant positive correlations with their knowledge of GenAI (r=0.189,p<0.001r = 0.189, p < 0.001) and with their frequency of GenAI use (r=0.326,p<0.001r = 0.326, p < 0.001).

  5. Knowl 5 — Quantitative Evaluation of Student Concerns Regarding Generative AI

    data/table

    Student concern levels regarding generative AI (GenAI) integration were measured on a 5-point Likert scale (1=Strongly disagree1 = \text{Strongly disagree} to 5=Strongly agree5 = \text{Strongly agree}) across N=399N = 399 university respondents:

    Concern Statement Mean SD
    Using generative AI technologies such as ChatGPT to complete assignments undermines the value of university education 3.15 1.17
    Generative AI technologies such as ChatGPT will hinder my development of generic or transferable skills such as teamwork, problem-solving, and leadership skills 3.10 1.23
    Generative AI technologies such as ChatGPT will limit my opportunities to interact with others and socialize while completing coursework 3.06 1.20
    I can become over-reliant on generative AI technologies 2.85 1.13

    Concern scores hovered around the neutral midpoint (2.85≤Mean≤3.152.85 \le \text{Mean} \le 3.15). Statistically significant differences in concern levels were observed between non-users/rare users and frequent users (t=3.873,p<0.01t = 3.873, p < 0.01). However, correlation analysis revealed no statistically significant relationship between students' GenAI concerns and their knowledge about GenAI technologies (r=0.096,p>0.05r = 0.096, p > 0.05).

  6. Knowl 6 — Survey Methodology and Thematic Analysis Setup for Assessing GenAI Perceptions

    experimental setup

    The investigation employed a cross-sectional mixed-methods survey targeting university students across six universities in Hong Kong:

    • Instrument Design: The survey comprised 26 Likert-scale items (scored from 1 = "Strongly disagree" to 5 = "Strongly agree") covering GenAI knowledge, adoption willingness, perceived challenges, and educational impact, alongside three open-ended qualitative questions. The instrument was iteratively refined through pilot testing prior to administration.
    • Sampling Strategy: Convenience sampling via an online platform yielded N=399N = 399 voluntary, anonymous responses spanning 10 academic faculties.
    • Quantitative and Qualitative Analysis: Descriptive statistics, independent-sample tt-tests, and Pearson correlation coefficients (rr) were computed on the Likert data. The qualitative corpus (n=387n = 387 valid open-ended responses) was evaluated via manual thematic analysis: two independent coders initially coded a shared subset of 50 responses, resolved discrepancies through consensus discussion, developed a standardized codebook, and subsequently completed the coding of the remaining dataset.

  7. Knowl 7 — Demographic Profile and Usage Distribution of Surveyed University Students

    data/table

    The study sample comprises N=399N = 399 university students across six tertiary institutions and ten faculties in Hong Kong:

    Characteristic Category n %
    Sex Male 204 51.1
    Female 195 48.9
    Academic Level Undergraduate 177 44.4
    Postgraduate 222 55.6
    Major STEM 221 55.4
    Non-STEM 173 43.4
    GenAI Usage Frequency Never 133 33.3
    (e.g., ChatGPT) Rarely 87 21.8
    Sometimes 116 29.1
    Often 39 9.8
    Always 24 6.0

    STEM students were primarily enrolled in Engineering (33.1%33.1\%) and Science (14.5%14.5\%), while Non-STEM participants were distributed across Arts (14.8%,n=5914.8\%, n = 59), Business (13.3%,n=5313.3\%, n = 53), and Education (7.5%,n=307.5\%, n = 30), with the remaining participants across Architecture, Dentistry, Law, Medicine, and Social Sciences. A total of 66.7%66.7\% of participants had used generative AI at least once in general contexts.

  8. Knowl 8 — Limitations of Student Perception Study on Generative AI

    limitation

    The research findings are subject to four primary limitations:

    1. Sample Scope and Geography: The sample size (N=399N = 399) collected via convenience sampling across universities in Hong Kong restricts generalizability to other geographical, cultural, and institutional settings.
    2. Self-Report and Recall Bias: Dependence on online self-reported survey responses introduces potential social desirability bias and imprecise subjective recall regarding GenAI frequency and proficiency.
    3. Cross-Sectional Architecture: The single-timepoint survey structure prevents longitudinal tracking of how student attitudes, literacy, and concerns evolve as GenAI tools mature and become embedded in formal curricula.
    4. Limited Exposure in Formal Curricula: At the time of data collection, GenAI tools were not systematically integrated into official university courses, meaning perceptions reflect informal, early-stage adoption rather than measured impacts on actual learning outcomes.

Coverage note — None was omitted; all primary empirical findings, survey datasets, thematic models, methodology, and limitations were extracted as self-contained knowls.

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Citation

MLA
Chan, C. K. Y., and W. Hu. “Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education”. arXiv, 2023, http://arxiv.org/abs/2305.00290v1.
APA
Chan, C. K. Y., & Hu, W. (2023). Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education. arXiv. http://arxiv.org/abs/2305.00290v1
Chicago
Chan, C. K. Y., and W. Hu. 2023. “Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education”. arXiv. http://arxiv.org/abs/2305.00290v1.
Harvard
Chan, C.K.Y. and Hu, W. (2023) “Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education”, arXiv [Preprint]. Available at: http://arxiv.org/abs/2305.00290v1.
Vancouver
1. Chan CKY, Hu W (2023) Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education. arXiv

BibTeX

@article{chan2023students,
  title = {Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education},
  author = {Chan, Cecilia Ka Yuk and Hu, Wenjie},
  year = {2023},
  journal = {arXiv},
  url = {http://arxiv.org/abs/2305.00290v1},
  eprint = {2305.00290}
}
Metadata:arXiv

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