BRS20103 Research Framework Sample: Factors Influencing Generative AI Adoption Among University Students and Its Effect on Learning Quality
For information and reference only – not to be submitted as your own work. This is a model extract written by our expert to show how the BRS20103 Assignment 1 sections can be structured. Submitting it, in whole or in part, as your own work breaches MSU academic integrity rules and the brief’s zero-mark plagiarism policy. Use it to understand the approach, then write your own chapters from your own reading.
Brief: BRS20103 Research Methodology Assignment 1 (September 2026) question. Step-by-step method: how to develop a research framework for Chapters 1 and 2.
Extract scope: selected parts of 1.2, 1.3, 1.4–1.5, 1.6, 2.2, 2.3, 2.4 and 2.5. A full submission also needs 1.1, 1.7, 1.8, 2.1, a longer synthesis and a matrix of 30–50 articles, as the brief requires.
1.2 Background of the Study (excerpt)
Generative artificial intelligence (GenAI) tools such as ChatGPT have moved from novelty to routine study aids within two academic years. A global survey by the Digital Education Council (2024) reported that 86% of students use AI in their studies and 54% use it weekly, with ChatGPT the most preferred tool. In Malaysia, an early study of 406 university students found that performance expectancy, effort expectancy, hedonic motivation and learning value shaped their intention to use ChatGPT for education (Foroughi et al., 2024).
Usage, however, is not the same as learning. Universities now need evidence on whether GenAI adoption improves the quality of students’ learning or simply speeds up task completion.
1.3 Problem Statement (Ideal vs Reality excerpt)
Ideal. In principle, GenAI should act as a virtual tutor that explains concepts, gives instant feedback and supports self-study, freeing students to spend more effort on higher-order thinking (Lo, 2023). Students themselves expect it to help with writing, research and personalised learning (Chan & Hu, 2023).
Reality. The evidence is less comfortable. In a three-wave study of 494 university students, Abbas et al. (2024) found that heavier academic workload and time pressure pushed students towards ChatGPT, and that higher use was associated with procrastination, self-reported memory loss and lower academic performance. Students also report concerns about accuracy, over-reliance and plagiarism (Chan & Hu, 2023), while the learning-outcome evidence reviewed by Lo (2023) remains limited.
Gap. Most adoption studies stop at behavioural intention or use (Habibi et al., 2023; Strzelecki, 2024a, 2024b), and Malaysian evidence is mostly limited to intention (Foroughi et al., 2024). Few studies test, in one model, which factors drive GenAI adoption among Malaysian private university students and whether that adoption is associated with perceived learning quality. This study addresses that gap.
1.4 and 1.5 Research Objectives and Research Questions
| No. | Research Objective (RO) | Research Question (RQ) | Hypothesis |
|---|---|---|---|
| 1 | To examine the relationship between performance expectancy and GenAI adoption among university students. | Does performance expectancy significantly influence GenAI adoption among university students? | H1 |
| 2 | To examine the relationship between effort expectancy and GenAI adoption among university students. | Does effort expectancy significantly influence GenAI adoption among university students? | H2 |
| 3 | To examine the relationship between facilitating conditions and GenAI adoption among university students. | Do facilitating conditions significantly influence GenAI adoption among university students? | H3 |
| 4 | To examine the relationship between GenAI adoption and perceived learning quality among university students. | Does GenAI adoption significantly influence perceived learning quality among university students? | H4 |
Note how each row repeats the same variable names. That is what the rubric means by “perfectly aligned”.
1.6 Definition of Key Terms
| Variable | Conceptual definition | Operational definition (this study) |
|---|---|---|
| Performance expectancy (IV) | The degree to which using a technology will provide benefits to users in performing certain activities (Venkatesh et al., 2012; based on Venkatesh et al., 2003). | Students’ agreement that GenAI tools help them complete coursework faster and better, measured with items adapted from Venkatesh et al. (2012) on a five-point Likert scale. |
| Effort expectancy (IV) | The degree of ease associated with users’ use of a technology (Venkatesh et al., 2012). | Students’ rating of how easy it is to learn and use GenAI tools for study tasks, five-point Likert scale. |
| Facilitating conditions (IV) | Users’ perceptions of the resources and support available to perform a behaviour (Venkatesh et al., 2012). | Students’ rating of their access to devices, internet, AI tool accounts, guidance and university AI-use policy, five-point Likert scale. |
| GenAI adoption (DV for H1–H3; predictor for H4) | Behavioural intention to use and actual use of a technology (Venkatesh et al., 2003, 2012). | Students’ stated intention to keep using GenAI for study and their self-reported frequency of use. |
| Perceived learning quality (DV) | Working definition for this study: a learner’s own judgement of the depth of understanding, feedback and independent thinking achieved through a learning activity. | Students’ rating of whether GenAI use improves their understanding, feedback received and ability to work independently, five-point Likert scale. |
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2.2 Underpinning Theory (excerpt)
This study is underpinned by the Unified Theory of Acceptance and Use of Technology (UTAUT). Venkatesh et al. (2003) consolidated eight earlier acceptance models into four core determinants of intention and use: performance expectancy, effort expectancy, social influence and facilitating conditions. UTAUT2 later extended the model to consumer contexts by adding hedonic motivation, price value and habit (Venkatesh et al., 2012).
UTAUT fits this topic better than the original Technology Acceptance Model (Davis, 1989) because it adds facilitating conditions. For GenAI, access to paid versions, devices and clear university rules is a real constraint, not a background detail. Social influence is not hypothesised here because Foroughi et al. (2024) found it non-significant among Malaysian students; it can be retained as a control in a fuller study.
UTAUT explains adoption but not learning outcomes. The path from adoption to perceived learning quality (H4) is therefore justified by the education literature reviewed in 2.3.4, which is itself a contribution.
2.3 Literature Review: Synthesis by Variable (excerpt)
2.3.1 Performance expectancy and GenAI adoption
Performance expectancy is the most consistent predictor across contexts. Strzelecki (2024b) found it the second strongest driver of intention among 534 Polish students, after habit, and a second sample of 503 students confirmed the pattern (Strzelecki, 2024a). Foroughi et al. (2024) reported the same positive effect in Malaysia. The agreement across European and Malaysian samples supports a positive hypothesis.
2.3.2 Effort expectancy and GenAI adoption
Findings here conflict. Foroughi et al. (2024) found effort expectancy significant in Malaysia, while Habibi et al. (2023) found no significant effect among 1,117 Indonesian students, and Strzelecki (2024a) reported only a minimal effect size. One explanation is that ChatGPT is now so easy to use that ease no longer separates users from non-users. Testing this in a Malaysian private university sample is useful precisely because the evidence is split.
2.3.3 Facilitating conditions and GenAI adoption
Facilitating conditions were the strongest predictor of intention in Indonesia (Habibi et al., 2023) but were not significant for intention in Poland, affecting use behaviour instead (Strzelecki, 2024b). In Malaysia, they were non-significant (Foroughi et al., 2024). The difference may reflect infrastructure and policy clarity, which vary between institutions.
2.3.4 GenAI adoption and learning quality
This is the least settled area. Lo (2023) describes potential tutoring and feedback benefits but notes limited outcome evidence. Chan and Hu (2023) found students positive about learning benefits yet concerned about over-reliance. Abbas et al. (2024) found higher ChatGPT use associated with lower academic performance. A directional positive hypothesis is retained because the study measures perceived learning quality, which students’ own reports suggest is positive, but the contrary evidence must be discussed in Chapter 5.
Literature Matrix Table (extract – 6 of the required 30–50 articles)
| Author | Year | Title of Article | Research Problem | Objectives | Research Design | Dependent Variables | Independent Variables | Sampling Method | Data Collection Method | Key Findings | Remarks |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Strzelecki | 2024b (online 2023) | To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology | Little evidence on why students accept ChatGPT | Identify UTAUT2 predictors of intention and use | Quantitative, PLS-SEM | Behavioural intention; use behaviour | PE, EE, SI, FC, HM, habit, personal innovativeness | 534 students, one Polish state university; technique to be read from the article’s method section | Survey questionnaire | Habit strongest for intention, then PE and HM; FC not significant for intention | Supports H1; mixed for H3 |
| Strzelecki | 2024a | Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology | Need to retest UTAUT2 for ChatGPT | Test extended UTAUT2 | Quantitative, PLS-SEM | Behavioural intention; use behaviour | UTAUT2 constructs + personal innovativeness | 503 students, Poland; technique to be read from the article | Survey questionnaire | Habit, PE and HM drive intention (72.8% variance); EE and SI minimal effects | Supports H1; weak for H2 |
| Habibi et al. | 2023 | ChatGPT in higher education learning: Acceptance and use | Acceptance of ChatGPT in Indonesian higher education | Apply UTAUT2 to ChatGPT learning use | Quantitative, PLS-SEM and IPMA | Behavioural intention; use | UTAUT2 constructs | 1,117 students, Indonesian HEIs; technique to be read from the article | Online survey (with pilot) | FC strongest for intention; EE not significant | Supports H3; contradicts H2 |
| Foroughi et al. | 2024 | Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA | Drivers of ChatGPT intention in Malaysia | Identify determinants and configurations | Quantitative, PLS-SEM + fsQCA | Intention to use ChatGPT | PE, EE, SI, FC, HM, habit, learning value | 406 students, Malaysia; technique to be read from the article | Survey questionnaire | PE, EE, HM and learning value significant; SI, FC, habit not significant | Closest context; supports H1, H2 |
| Chan & Hu | 2023 | Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education | Unclear student perceptions of GenAI | Explore perceptions, benefits and concerns | Mixed (survey + thematic analysis) | Willingness to use; perceptions | Knowledge, frequency of use | 399 students, six Hong Kong universities; technique to be read from the article | Online survey with open questions | Positive about learning benefits; concerns on accuracy, privacy, over-reliance | Supports H4 (perceived benefit) with caveats |
| Abbas et al. | 2024 | Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students | Causes and consequences of ChatGPT use unknown | Develop a usage scale; test antecedents and outcomes | Quantitative, three-wave time-lagged | Procrastination, memory loss, academic performance | Workload, time pressure, reward and quality sensitivity | 165 (Study 1) and 494 (Study 2) university students; technique to be read from the article | Surveys at three time points | Use linked to procrastination, memory loss, lower performance | Contrary evidence for H4; use in problem statement |
PE = performance expectancy; EE = effort expectancy; SI = social influence; FC = facilitating conditions; HM = hedonic motivation.
2.4 Hypotheses Development
- H1: There is a significant positive relationship between performance expectancy and GenAI adoption among university students.
- H2: There is a significant positive relationship between effort expectancy and GenAI adoption among university students.
- H3: There is a significant positive relationship between facilitating conditions and GenAI adoption among university students.
- H4: There is a significant positive relationship between GenAI adoption and perceived learning quality among university students.
2.5 Theoretical and Conceptual Framework
Theoretical framework: UTAUT/UTAUT2 (Venkatesh et al., 2003, 2012) → performance expectancy, effort expectancy, facilitating conditions → H1–H3; adoption → perceived learning quality (education literature) → H4.
Figure 2.1 Conceptual framework (draw as boxes and arrows in Word or draw.io):
| Independent Variables (IV) | Path | Intervening Variable | Path | Dependent Variable (DV) |
|---|---|---|---|---|
| Performance Expectancy | — H1 → | GenAI Adoption (intention and use) | — H4 → | Perceived Learning Quality |
| Effort Expectancy | — H2 → | |||
| Facilitating Conditions | — H3 → |
Source: Adapted from Venkatesh et al. (2003, 2012). If your lecturer prefers a strict IV → DV diagram, present adoption as the DV and treat learning quality as a separate RO.
Want this level of structure for your own topic? Send us your approved title and we will guide you through Chapters 1 and 2. Get BRS20103 expert support or use the framework guide linked at the top of this page.
Related resources
See another model on a technology-adoption topic in our adoption of e-wallet assignment sample.
About the expert
Amirul Azam holds an MBA from the University of Malaya. His profile lists marketing, management, organizational behaviour and international business, over a decade in managerial roles in marketing and advertising, six years of academic assistance and 2,341 completed orders.
References
Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, Article 10. https://doi.org/10.1186/s41239-024-00444-7
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Digital Education Council. (2024, August). How students use AI: The evolving relationship between AI and higher education [Global AI Student Survey 2024]. https://www.digitaleducationcouncil.com/post/how-students-use-ai-the-evolving-relationship-between-ai-and-higher-education
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2024). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495
Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, Article 100190. https://doi.org/10.1016/j.caeai.2023.100190
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410
Strzelecki, A. (2024a). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49, 223–245. https://doi.org/10.1007/s10755-023-09686-1
Strzelecki, A. (2024b). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 32(9), 5142–5155. https://doi.org/10.1080/10494820.2023.2209881
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
