Perception and Critical Thinking as Correlates of Artificial Intelligence Utilisation: A Case Study of University of Ibadan Undergraduates

1. Introduction

The introduction of Artificial Intelligence (AI) into various aspects of society is rapidly transforming the global landscape, reshaping the way individuals learn, communicate, and solve problems. In the educational sector, particularly at the university level, AI learning tools have become instrumental in enhancing research, personalized learning, data analysis, and administrative efficiency (Chen et al., 2022). As such, undergraduates are increasingly expected not only to be aware of AI but also to effectively utilise its capabilities in their academic and everyday pursuits. However, the extent to which these technologies are utilised is often influenced by undergraduates’ perception and critical thinking abilities.

Perception, as a cognitive and psychological factor, plays a critical role in shaping how individuals’ approach and adopt new technologies. Undergraduates who perceive AI as beneficial and accessible are more likely to engage with it constructively. Conversely, misconceptions, fear of job loss, or ethical concerns may hinder acceptance, especially in regions where digital literacy is still emerging.

On the other hand, critical thinking ability is defined as the capacity to analyze, evaluate, and synthesize information. It has been identified as a key determinant in navigating complex technologies such as AI. According to Moustaghfir and Brigui (2024), individuals with strong critical thinking skills are better positioned to make informed decisions and adapt to rapidly changing digital environments. This suggests that undergraduates that possess digital literacy skills with critical thinking ability will be favorably disposed to using AI tools for their academic activities. In the African context, however, the development of critical thinking among undergraduates remains a challenge due to rote-based learning traditions and limited exposure to problem-solving curricula (Amevor, 2025).

Perception and critical thinking are the independent variable in this study is grounded in both theoretical and empirical considerations. Theoretically, the Technology Acceptance Model (TAM) posits that perceived usefulness and perceived ease of use are the most fundamental determinants of technology adoption (Davis, 1989) and these indicators have been commonly used across various researches on technology and its usage, this therefore, suits the support the dependent variable; utilisation of AI learning tools. Empirically, recent studies have established that undergraduates’ perceptions of AI significantly influence their willingness to use AI tools (Omirali et al., 2025). However, perception alone does not account for proper usage, undergraduates must also have critical thinking ability to fact-check AI outputs critically. Critical thinking has been identified as a key determinant in using complex technologies and using AI responsibly (Facione, 2015; Yatani et al., 2024). In the Nigeria region, where surface learning and mechanical learning styles have limited the critical thinking ability of undergraduates (Amevor, 2025), understanding the role of critical thinking in AI utilisation is needed especially with the continuous growth in the numbers of AI learning tools to the educational setting.

Despite the growing presence of AI in higher education, there remains a knowledge gap in understanding how cognitive attributes such as perception and critical thinking intersect to influence AI utilisation among undergraduates. While international research highlights the importance of these factors, empirical studies from African universities and particularly in Nigeria are scarce. This study, therefore, seeks to explore how perception and critical thinking ability determine the extent to which undergraduates in university of Ibadan, Nigeria engage with AI tools, thereby providing insights for educators, policymakers, and technology developers.

2. Statement of the Problem

The era of teacher–centered and conventional educational system of learning with little encouragement for undergraduates’ participation in teaching and learning process is being globally challenged with the introduction of technology in education. Artificial Intelligence (AI) has brought untold changes in education, assisting undergraduates in information searching and retrieval, academic writing and research offering personal assistance, writing research paper.

Despite the increasing availability of AI learning tools, there appears to be a considerable gap in their effective utilisation among undergraduates, especially in developing countries. In many African universities, undergraduates often underutilize AI learning tools due to negative perceptions, or lack of the critical thinking skills necessary for effective usage.

In spite low level of AI adoption by undergraduates in some African countries, global research has identified the role of cognitive factors in technology adoption, especially on intelligent use of AI tools. However, without clear understanding of how psychological variables like perception and critical thinking can influence use of AI learning tools, efforts to integrate artificial intelligence into academic environments may be ineffective or underutilised.

Therefore, this study seeks to fill the knowledge gap by investigating the level to which perception and critical thinking ability could predict AI utilisation among undergraduates in universities.

3. Objectives of the Study

The main objective of this study is to investigate the extent to which perception and critical thinking ability correlate with the utilisation of artificial intelligence learning tools among undergraduates in university of Ibadan, Nigeria. The specific objectives are to:

∎ Ascertain the perception of Artificial Intelligence among the undergraduates;
∎ assess the level of critical thinking ability of undergraduates in relation to AI utilization;
∎ ascertain the extent of utilisation of Artificial Intelligence tools by the undergraduates;
∎ determine the relationship between undergraduates’ perception of AI and their utilisation of AI learning tools;
∎ establish the relationship between critical thinking ability and the utilisation of AI among undergraduates.
∎ find out the joint and relative contribution of perception and critical thinking ability to the utilisation of artificial intelligence among undergraduates

4. Research Questions

∎ What is the perception of Artificial Intelligence among the undergraduates?
∎ What is the level of critical thinking ability of undergraduates in relation to AI utilization?
∎ What is the extent of utilisation of Artificial Intelligence tools by the undergraduates?
∎ What is the relationship between undergraduates’ perception of AI and their utilisation of AI learning tools?
∎ What is the relationship between critical thinking ability and the utilisation of AI among undergraduates?
∎ What is the joint and relative contribution of perception and critical thinking ability to the utilisation of artificial intelligence among undergraduates?

5. Research Hypotheses

The following null hypotheses were tested at 0.05 level of significance;

H1: There is no significant positive relationship between perception and utilisation of AI learning tools among undergraduates. H2: There is a significant positive relationship between critical thinking ability and utilisation of AI learning tools among undergraduates.

6. Literature Review

Perception plays a foundational role in the adoption of emerging technologies as undergraduates’ understanding and attitude toward AI largely determine whether they will engage with it meaningfully. The apprehension of undergraduates resulting from lack of thorough understanding and potential benefits of use of artificial intelligence might engender a mixture of hope and fear. However, Baker and Smith., (2019) reveals that hope lies in the potential of artificial intelligence to personalise learning, provide 24/7 academic support, and prepare them for a technology driven future. In addition, Pikhart and Al-Obaydi (2025) stresses that there is also fear regarding the replacement of human teachers, misuse of personal data, and a potential decline in social interactions. According to Gerlich (2023), understanding and addressing these perceptions are crucial for maximising the acceptance and effectiveness of artificial intelligence in education. In the light of the diversities in undergraduates’ perception about AI, there is need for a balanced approach that leverages artificial intelligence's potential while addressing its associated fears.

Studies have shown that undergraduates who perceive AI as beneficial and non-threatening are more likely to integrate it into their academic routines (Omirali et al., 2025). Similarly, positive perceptions of artificial intelligence's ability to enhance learning and adapt to users' preferences can positively impact its utilisation for educational purposes (Sumakul et al.,2022). In Africa, the perception of AI is still evolving, with studies showing that many undergraduates lack comprehensive awareness of AI applications, limiting their willingness and ability to explore its full potential (Adarkwah et al., 2023).

Awareness of AI learning tools in Africa remains relatively low, with many undergraduates exhibiting limited understanding of AI applications beyond social media algorithms or digital assistants (Sanusi et al., 2023). Factors such as misinformation, lack of exposure, and limited integration into curricula contribute to these perception gaps.

In the context of artificial intelligence utilisation, critical thinking enables individuals to analyze, evaluate, and make informed decisions about the outputs and implications of artificial intelligence systems. Facione (2015) emphasizes that critical thinkers are better equipped to evaluate the ethical implications and practical limitations of AI tools. Low level of critical thinking may reduce undergraduates ' ability to interpret, challenge, and maximize AI systems critically. Critical thinking is the process of thinking about ideas or situations in order to understand them fully, identify their implications, make a judgment, and/or guide decision making. This imply that undergraduates that can think critically would be able to critique the relevance of information retrieved with AI and make decisions on whether to use the information or discard it. Undergraduates who are taught these skills become critical thinkers who can move beyond superficial conclusions to a deeper understanding of the issues they are examining (Lai, 2011)

Critical thinking improves responsible and ethical use of artificial intelligence, especially in academic research, decision-making, and creative problem-solving. Undergraduates with higher critical thinking skills can detect misinformation, assess the credibility of AI outputs, and also avoid misuse. In contrast, limited cognitive engagement may lead to over-reliance on AI tools or superficial use without understanding underlying mechanisms. Current studies suggest a positive correlation between higher-order cognitive skills and the ability to engage with AI in a purposeful manner (Yatani et al., 2024).

The global academic sector is experiencing a growing usage of AI learning tools, including intelligent tutoring systems, plagiarism detection software, predictive analytics, and adaptive learning platforms (Ogalo and Mtenzi, 2025; Holmes et al., 2022). Despite this trend, studies show that actual usage among undergraduates is still inconsistent. In Nigeria, for instance, a study found that only 28% of surveyed undergraduates had used AI tools beyond social media or digital keyboards (Ezeanya et al., 2024). Factors such as poor internet infrastructure, lack of digital literacy, and inadequate institutional support contribute to the low utilisation. Fakuade et al. (2023) study on undergraduates’ utilisation of Artificial Intelligence (AI) for learning revealed that undergraduates often use AI tools for learning. However, AI adoption emerged at various, high and low level of adoption.

Similarly, Onyeka et al. (2024) study carried out on undergraduates’ use of AI tools has shown that a substantial majority of undergraduates (59.6%) use AI applications daily, with an additional 34.6% utilizing them weekly. This suggests a high level of integration of AI tools into the academic routines of undergraduates. However, undergraduates with negative or skeptical views about AI tend to avoid it, while those with optimistic perceptions are more proactive in exploring its possibilities (Nkedishu, and Okonta, 2024; Zawacki-Richter et al., 2019). Culturally-induced fears and socio-economic limitations often color undergraduates’ attitudes toward AI in developing countries. Perception influences the utilisation of artificial intelligence learning tools as affirmed by Gerlich (2023) which asserted that perception shapes individuals' trust in artificial intelligence technologies, influencing their acceptance or resistance to incorporating artificial intelligence into various aspects of their lives.

7. Theoretical Framework

This study used two theoretical framework to support each of the variables. First, the Technology Acceptance Model (TAM) proposed by Davis (1989) was adopted to explain undergraduates' perceptions and use of AI learning tools. TAM states that perceived usefulness and perceived ease of use are determinants of use toward technology adoption (Davis, 1989). The two indicators of this theory are; perceived usefulness and perceived ease of use. Perceived usefulness is shown in undergraduates' beliefs that AI learning tools boosts their academic effectiveness and improves their performance, while perceived ease of use is the user-friendliness, clear functionalities, and interface clarity of the AI learning tools. TAM provides a unified framework that explains both how undergraduates perceive AI learning tools and why they use it. The indicators; perceived usefulness and ease of use will be used to support these two variables in this study (Perception and utilisation of AI learning tools). Thus, treating them as conceptually different. The application of TAM to explain both perception and utilisation is well-established in the literature as several studies have successfully employed TAM to measure undergraduates' perceptions of technology and predict their utilisation of AI. This study follows the same established tradition.

John Dewey's (1933) theory of reflective thinking was adopted to conceptualise critical thinking ability. Dewey argued that true learning occurs when individuals move beyond passive acceptance of information to active inquiry, questioning, and evaluation. In this study, critical thinking is operationalised through three indicators: experience (connecting AI information to personal knowledge), reflective thinking (considering ethical implications and institutional policies), and quest for meaning (seeking additional resources, verifying claims, and ensuring outputs are meaningful).

In times when AI output is misleading or biased, positive perceptions without critical thinking ability may lead to misuse rather than empowering undergraduates; therefore, critical thinking complements TAM theory by assisting the undergraduates to question AI's output, verify its claims, and use it appropriately and ethically. While TAM predicts initial adoption and use, critical thinking helps in the depth of usage to make sure that undergraduates are not passive but active in the use of AI learning tools. Critical thinking ability in this research is indicated through three key indicators: experience (undergraduates connecting their personal experiences to AI-generated information and clarifying confusing outputs through real life issues), reflective thinking (undergraduates reflecting on AI prompts and considering the institution/ university ethical policy), and the quest for meaning (undergraduates seeking additional resources to deepen their support AI claims, determining what information is worth using, editing and rephrasing AI outputs, and going the extra mile to ensure their final answers are meaningful).

While TAM provides a foundational lens for understanding technology use, recent scholars have called for theoretical extensions that accommodate the unique characteristics of AI learning tools and technologies. Unlike earlier technologies, AI tools are generative, adaptive in their decision-making processes and this has called for the development of AI- specific frameworks with indicators like trust, transparency, and ethical concerns (Glikson & Woolley, 2020; Kaur et al., 2024). While this study does not use this framework, their relevance is stated by incorporating critical thinking as a complementary construct. Critical thinking addresses the need for carefulness and evaluation when using AI learning tools and this is a dimension that TAM alone does not fully capture.

8. Methodology

9. Results Analysis

Research question 1. What is the perception of Artificial Intelligence among the undergraduates?

Table 1 presents information on the perception of artificial intelligence learning tools among undergraduates of the University of Ibadan.

Table 1.

Perception of Artificial Intelligence among the Undergraduates

Table 1 above indicates the perception of undergraduates towards artificial intelligence in University of Ibadan. Each perception is based on respondents’ rating which indicates levels of agreement with the items. The perception with the highest mean score is: AI provided me with a lot of enjoyment and ease (3.72, SD = .465) and then followed by the perception that learning to use the AI apps interface was easy to use for me (3.69, SD= .546). The perception with the lowest mean score is: I found the AI apps to be flexible to interact with (3.56, SD = 0.594). The weighted mean score which is 3.62 is higher than the criterion mean of 2.50. Therefore, it can be concluded that the perception of undergraduates of university of Ibadan about using AI tools for educational purposes is positive.

Research Question 2. What is the level of critical thinking ability of undergraduates in relation to AI utilization?

Table 2 presents information on the level of the critical thinking ability of undergraduates in University of Ibadan.

Table 2.

Level of Critical Thinking Ability of Undergraduates

Table 2 above indicates the level of critical thinking ability of undergraduates when utilising artificial intelligence learning tools. The critical thinking ability with the highest mean score is: when using AI I feel comfortable when asking questions/ information (3.74, SD = .455) and then followed by ‘I connect my personal experiences and information provided by AI learning tools.’ (3.57, SD= .587). The critical thinking ability with the lowest mean score is: AI do not always address the questions I ask (2.25, SD = 1.211), this is low, it shows that many of the respondents do not agree to this, meaning that they believe, artificial intelligence learning tools address their questions effectively. The weighted mean score is 3.42 while the criterion mean is 2.50. Because the weighted mean is higher than the criterion mean, the critical thinking ability level of the undergraduates is high.

Research question 3: What is the extent of utilisation of Artificial Intelligence tools by the undergraduates?

Table 3 revealed the extent of use of artificial intelligence learning tools by undergraduates.

Table 3.

Extent of utilisation of Artificial Intelligence tools by the undergraduates

As shown in Table 3, ChatGPT is the most frequently used AI tool with a mean score of 3.62 while Turnitin is the least frequently used AI tools with mean score of 2.72. Although the extent of use of the artificial intelligence learning tools by undergraduates varies across different artificial intelligence learning tools, the weighted mean of 2.97 is lower than the criterion mean of 3.0. This indicates that the overall level of AI tools utilisation among undergraduates is low.

Research Question 4: What is the joint and relative contribution of perception and critical thinking ability to the utilisation of artificial intelligence among undergraduates?

Table 4 presents the summary of multiple regression analysis showing the relative contribution of perception and critical thinking ability to the utilisation of artificial intelligence among undergraduates of the university of Ibadan.

a. Dependent variable: utilization of artificial intelligence learning tools

Note: R² = .004, F(2, 639) = 4.27, p = .014

As shown in Table 6, when both perception and critical thinking ability were entered simultaneously, only critical thinking ability emerged as a significant independent predictor of AI utilisation (β = .066, t = 3.154, p = .002). while perception was not statistically significant (β = -.002, t = -.075, p = .940).

This finding suggests that while perception shows a significant relationship between perception and critical thinking ability with AI usage, and according to the table critical thinking ability contributed largely to the usage of AI. This implies that an undergraduate may have a positive perception about AI, but without critical thinking ability (reflective thinking, the quest for meaning and experience) he/ she is less likely to use AI learning tools. In other words, an undergraduates equipped with critical thinking ability are better positioned and more likely use and navigate AI learning tools regardless of their perception.

Hypothesis 1: There is no significant relationship between perception and utilisation of artificial intelligence tools among undergraduates of University of Ibadan;

Table 5 showing the Relationship between perception and utilisation of artificial intelligence among undergraduates.

The results shown in Table 5 above indicate that the relationship between perception and utilisation of AI learning tools is weak, positive, and significant (r= .092 , p < 0.05). Since P value which is 0.02 is lower than 0.05 level of significance, the null hypothesis that states : there is no significant relationship between perception and utilisation of AI learning tools is rejected. This implies that undergraduates with more positive perception of AI tend to use AI learning tools more frequently, although the relationship is weak.

H02 : There is no significant relationship between critical thinking ability and utilisation of artificial intelligence among undergraduates.

Table 6 showing the relationship between critical thinking ability and utilization of artificial intelligence among undergraduates.

It is indicated by the results provided in Table 6 that the relationship between critical thinking ability and utilization of AI is significant, positive, weak and significant (r= .094, p < 0.05) . The significant p-value is 0.000 which is lower than the 0.05 level of significance. As a result, the null hypotheses that states that there is no significant relationship between critical thinking ability and utilisation of AI learning tools among undergraduates of university of Ibadan is rejected. This suggests that an increase in critical thinking ability will increase the utilisation of AI learning tools among undergraduates, that is, undergraduates with higher critical thinking ability tend to use AI learning tools more frequently.

10. Results and Discussion

This section presents the results of the study as stated in the research questions and hypotheses. All tables referenced in this section are presented in the accompanying TABLES.docx file.

11. Conclusion

The study revealed that the perception of AI among the undergraduates was positive while their critical thinking ability was also high when it comes to application of AI. Undergraduates who had a more positive perception of AI were more likely to use these technologies into their academic activities The study found that while undergraduates are generally aware of AI and have a positive perception of its potential, their utilisation of AI tools remains limited. The high- order cognitive ability of the undergraduates correlates with their use of AI learning tools for various purposes. It is evident from the findings of this study that perception and critical thinking ability of the undergraduates are among the key factors to be considered in understanding the adoption and use of Artificial Intelligence in education by the undergraduates. The regression analysis also indicated that although perception correlates with AI use, it is critical thinking ability that solely predicts sustained the usage. This implies that positive attitudes toward AI is insufficient; universities must encourage and teach critical thinking ability to drive meaningful AI usage. These findings emphasizes the need for institutions to provide better support for the adding of AI into the curriculum and ensure that undergraduates have the necessary resources and skills to use AI learning tools effectively.

While similar researches on AI use have been conducted in other parts of the world, this study contributes to the knowledge by providing empirical evidence from a Nigerian university system, a country where empirical research on AI utilisation is limited. The study's contribution to knowledge is not just in its theoretical findings but location specific. Therefore, providing significant knowledge and solutions to policymakers, educators, and technology developers in developing-country university systems like Nigeria.

12. Limitations of the study

There are several limitations on this research. The results may not be generally applicable due to the single institution focus and the demographic mix as the sample, which may not be representative enough. A larger size for future studies across various universities may be more generalized.

Furthermore, a cross-sectional design may limit the ability to establish causality between perception, critical thinking ability and the use of AI learning tools. Longitudinal or experimental designs will provide stronger evidence of the causal relationships between these variables. Also, employing mixed-method techniques that combine qualitative and quantitative methods will provide a deeper understanding of the variables.

Also, the use of self-developed structed questionnaire for critical thinking ability may not fully capture students' actual critical thinking performance, as self-reports are subject to social desirability and recall biases, therefore, future studies should consider using performance-based critical thinking assessments.

13. Recommendations

Based on the findings of this study, the following recommendations are made:

For knowledge content development, universities should not only teach how to use AI but also train undergraduates to critically evaluate AI-generated content, this will in turn make information produced by universities credible, novel, relevant and unbiased. Universities need to organise awareness programs and workshops to educate undergraduates about the various applications of AI in their academic and professional lives. This would dispel misconceptions associated with the use of AI and enhance the positive perception of AI learning tools by the undergraduates. In addition, the university must endeavour to review the curriculum by including AI and critical thinking and this must be strengthened with teaching and training the undergraduates on the use of AI. AI literacy and critical thinking would empower the undergraduates to make informed decisions about the use of AI learning tools.

Universities should facilitate discussions on the ethical implications of AI, encouraging undergraduates to think critically about AI’s societal impact. This will equip undergraduates with the tools to use AI responsibly and ethically in their academic and professional careers.

Specifically, universities should provide reliable internet access and computer laboratories with AI software and learning tools to ensure all undergraduate, regardless of socioeconomic background, have the access to these tools and to bridge digital divide. Universities should also organise practical workshops on AI literacy to teach ethical use and how to evaluate AI-generated content for accuracy, bias, and ethical implications.

Universities should also develop and disseminate university-wide guidelines on the ethical use of AI in academic work, clarifying what constitutes acceptable use, plagiarism, and academic integrity in the context of AI tools. Lecturers should be trained on how to incorporate AI tools in their teaching delivery and how to guide undergraduates' critical thinking while using AI learning tools.

Statements and Declarations

Author Contributions: Conceptualization, I.P.S. and A.S.O.; methodology, I.P.S. and A.S.O.; data collection, I.P.S.; analysis, I.P.S. and A.S.O.; writing—original draft preparation, I.P.S.; writing—review and editing, I.P.S. and A.S.O.; supervision, A.S.O. All authors have read and agreed to the published version of the manuscript.

All authors have read and agreed to the published version of the manuscript.

Funding: This research received no external funding.

Data Availability Statement: The data presented in this study are available on reasonable request from the corresponding author.

Institutional Review Board Statement: Not applicable. The study involved anonymous survey responses with no invasive procedures: however, ethical protocols were observed in line with the University of Ibadan's manual of writing.

Informed Consent Statement: While formal ethical approval was not required for this study as it involved anonymous survey responses with no invasive procedures, the researchers strictly followed the ethical guidelines outlined in the University of Ibadan's manual of writing. These guidelines include principles of voluntary participation, anonymity, confidentiality, and the right to withdraw from the study at any time without penalty. The researchers also made sure that no identifying information was collected from participants and above all informed consent was obtained from all participants involved in the study.

Conflicts of Interest: The authors declare no conflicts of interest.

Generative AI Use Statement: Generative AI tools were used in the preparation of this manuscript. The authors used ChatGPT-4 (OpenAI) minimally and reference management tools for literature search, identifying relevant papers, and formatting citations. No AI tools were used for data analysis, statistical computation, or manuscript drafting. All AI-generated suggestions were critically reviewed, verified, and edited by the authors, who take full responsibility for the accuracy, originality, and integrity of the final manuscript.

References

• 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]
• Adewusi, M. A., Ainebyoona, C., Odekeye, O. T., & Ngoma, M. (2025). AI in universities: The good, the bot, and the ugly truths [Preprint]. Research Square. [https://doi.org/10.21203/rs.3.rs-6883202/v1]
• Amevor, E. (2025). Strengthening teacher training and development in Ghana: Transforming education for national progress. Online Submission. ERIC.
• Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted
• Bensalem, E., Harizi, R., & Boujlida, A. (2024). Exploring undergraduate students' usage and perceptions of AI writing tools. Global Journal of Foreign Language Teaching, 14(2), 53–65. [https://doi.org/10.18844/gjflt.v14i2.9344]
• Cao, X., Lin, Y. J., Zhang, J. H., Tang, Y. P., Zhang, M. P., & Gao, H. Y. (2025). Students' perceptions about the opportunities and challenges of ChatGPT in higher education: A cross-sectional survey based in China. Education and Information Technologies, 30, 12345–12364. [https://doi.org/10.1007/s10639-025-13339-5]
• Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology and Society, 25(1), 28–47.
• 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]
• Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educative process. D.C. Heath and Company.
• Ezeanya, C. U., Ukaigwe, J. A., Ogbaga, I. N., & Kwanashie, A. (2024). Enhancing social engagement among online learners using AI-driven tools: National Open University of Nigeria learners' perspective. ABUAD Journal of Engineering Research and Development (AJERD), 7(2), 78–85. [https://doi.org/10.53982/ajerd.2024.0702.08-j]
• Facione, P. A. (2015). Critical thinking: What it is and why it counts. Insight Assessment. https://www.insightassessment.com/
• Fakuade, O. V., Alabi, A. J., Olubunmi, O. A., & Lawani, E. (2023). Assessment of undergraduates' utilisation of artificial intelligence for learning at Kampala International University, Kampala, Uganda. Journal of West European Social Sciences, 2(2). [https://doi.org/10.63632/jowess.v2i2.89]
• Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: A multi-dimensional study. Social Sciences, 12(9), 502. [https://doi.org/10.3390/socsci12090502]
• Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. [https://doi.org/10.5465/annals.2018.0057]
• Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. [https://doi.org/10.1111/ejed.12533]
• Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2024). Trustworthy artificial intelligence: A review. ACM Computing Surveys, 55(2), 1–38. [https://doi.org/10.1145/3491209]
• Lai, E. R. (2011). Critical thinking: A literature review. Pearson Assessment & Information. https://www.pearsonassessments.com/
• Li, Y., Tolosa, L., Rivas-Echeverria, F., & Marquez, R. (2025, March 18). Integrating AI in education: Navigating UNESCO global guidelines, emerging trends, and its intersection with sustainable development goals [Preprint]. ChemRxiv. [https://doi.org/10.26434/chemrxiv-2025-wz4n9]
• Moustaghfir, S., & Brigui, H. (2024). Navigating critical thinking in the digital era: An informative exploration. International Journal of Linguistics, Literature and Translation, 7(1), 137–143. [https://doi.org/10.32996/ijllt.2024.7.1.11x]
• Nkedishu, V. C., & Okonta, V. (2024). Unpacking optimism versus concern: Tertiary students' multidimensional views on the rise of artificial intelligence (AI). International Research Journal of Multidisciplinary Scope, 5, 362–377. [https://doi.org/10.47857/irjms.2024.05i04.01261]
• Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
• Ogalo, E. O., & Mtenzi, F. (2025). Leveraging artificial intelligence tools for learning: Academic integrity and ethics in higher education in Kenya. In Artificial Intelligence, Digital Learning, and Leadership: Redefining Higher Education (pp. 1–36). IGI Global. [https://doi.org/10.4018/979-8-3373-0025-2.ch001]
• Oluwadiya, K. S., Adeoti, A. O., Agodirin, S. O., Nottidge, T. E., Usman, M. I., Gali, M. B., Onyemaechim, N. O., Ramat, A. M. & Zakari, L. Y. U. (2023). Exploring artificial intelligence in the Nigerian medical educational space: An online cross-sectional study of perceptions, risks and benefits among students and lecturers from ten universities. Nigerian Postgraduate Medical Journal, 30(4), 285–292. [https://doi.org/10.4103/npmj.npmj_186_23]
• Omirali, A., Kozhakhmet, K., & Zhumaliyeva, R. (2025). Digital trust in transition: Student perceptions of AI-enhanced learning for sustainable educational futures. Sustainability, 17(17), 7567. [https://doi.org/10.3390/su17177567]
• Onyeka, N. C., Fabiyi, A. O., & Ateko, B. C. (2024). Exploring the usage of AI applications amongst students of Federal Polytechnic Ede, Osun State. Technology (IjOSEET), 16(16), 1–7.
• Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000366994
• Pikhart, M., & Al-Obaydi, L. H. (2025). Reporting the potential risk of using AI in higher education: Subjective perspectives of educators. Computers in Human Behavior Reports, 18, 100693. [https://doi.org/10.1016/j.chbr.2025.100693]
• Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023). Developing middle school students' understanding of machine learning in an African school. Computers and Education: Artificial Intelligence, 5, 100155. [https://doi.org/10.1016/j.caeai.2023.100155]
• Sarfo, J. O., Tachie-Donkor, G., Aggrey, E. K., & Mordi, P. (2024). Attitudes, perceptions, and challenges towards artificial intelligence adoption in Ghana and Nigeria: A systematic review with a narrative synthesis. International Journal of Media and Information Literacy, 9(2), 437–452. [https://doi.org/10.13187/ijmil.2024.2.437]
• Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13–35. [https://doi.org/10.1016/j.compedu.2018.09.009]
• Sumakul, D. T. Y., Hamied, F. A., & Sukyadi, D. (2022). Students' perceptions of the use of AI in a writing class. In 67th TEFLIN International Virtual Conference and the 9th ICOELT 2021 (TEFLIN ICOELT 2021) (pp. 52–57). Atlantis Press. [https://doi.org/10.2991/assehr.k.220201.009]
• Vázquez-Parra, J. C., Henao-Rodríguez, C., Lis-Gutiérrez, J. P., & Palomino-Gámez, S. (2024). Importance of university students' perception of adoption and training in artificial intelligence tools. Societies, 14(8), 141. [https://doi.org/10.3390/soc14080141]
• Yatani, K., Sramek, Z., & Yang, C. L. (2024). AI as extraherics: Fostering higher-order thinking skills in human-AI interaction [Preprint]. arXiv. https://arxiv.org/abs/2409.09218
• Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. [https://doi.org/10.1186/s41239-019-0171-0]