Factors Influencing AI Remote Proctoring Acceptance in Ghanaian Distance Education: A Mixed-Methods UTAUT Analysis

Factors Influencing AI Remote Proctoring Acceptance in Ghanaian Distance Education: A Mixed-Methods UTAUT Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors Influencing AI Remote Proctoring Acceptance in Ghanaian Distance Education: A Mixed-Methods UTAUT Analysis Sherry Adomah Bempah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6924498/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The COVID-19 pandemic accelerated the adoption of online education across Africa, necessitating innovative approaches to assessment integrity. This study explores Ghanaian students' and faculty perceptions of AI Remote Proctoring Systems (AIRPS) in distance education using the Unified Theory of Acceptance and Use of Technology (UTAUT). A concurrent mixed-methods design (n=370) revealed that performance expectancy (β=0.37, p<.001) and effort expectancy (β=0.31, p<.001) were the strongest predictors of adoption intention, followed by social influence (β = 0.17, p = .008). However, facilitating conditions were not significant. Contrary to expectations, demographic variables did not moderate these relationships. Findings suggest the need for training protocols that emphasize AIRPS benefits over perceived barriers in the Ghanaian distance education space. AI proctoring UTAUT distance education online assessment educational technology technology acceptance Ghana Figures Figure 1 1.0 Introduction The COVID-19 pandemic has fundamentally transformed the delivery of higher education worldwide, compelling institutions to rapidly migrate courses online to ensure educational continuity (Pokhrel & Chhetri, 2021 ). While this digital pivot accelerated the global adoption of e-learning, it simultaneously highlighted significant disparities between regions. In Europe, approximately 85% of higher education institutions (HEIs) successfully transitioned to online instruction, compared to only 29% in Africa (Mariononi et al., 2020). This digital divide extends beyond course delivery to assessment practices, where ensuring academic integrity in remote settings presents particular challenges for institutions with limited technological infrastructure. In Ghana, universities offering distance education programs have experimented with various online assessment approaches during and after the pandemic (Sedofia & Ampadu, 2022 ; Baidoo-Anu et al., 2023 ). However, the adoption of sophisticated technologies like Artificial Intelligence Remote Proctoring Systems (AIRPS) remains limited. The Ghana Tertiary Education Council's recent survey reveals that AI-based proctoring remains largely unexplored in Ghana, with only two institutions currently implementing such solutions within their learning management systems (GTEC, 2023). AIRPS represents an educational assessment innovation designed to ensure academic integrity in online examination environments. The system utilizes audio-visual surveillance technology and biometric authentication to monitor test-takers and their surrounding environment through webcams, flagging suspicious activities for review (Bedford et al., 2009 ). Research on AIRPS implementation has grown in Western contexts, yielding diverse reactions regarding its effectiveness and acceptance (Hylton et al., 2016 ; Hall et al., 2021 ). However, little is known about how these systems are perceived within the Ghanaian higher education landscape, particularly in distance education settings. In the post-pandemic educational landscape, online assessments are expected to persist, with Artificial Intelligence Remote Proctoring Systems (AIRPS) gaining attention among higher education institutions (HEIs) worldwide (Swauger,2020). Some studies highlight AIRPS’s potential to enhance assessment integrity and invigilation (Bedford et al., 2011; Boyd et al., 2016; Hylton et al., 2016 ; Karim et al., 2014 ; Bloemers et al., 2016 ; Berhane et al., 2018; Kitahara & Westfall, 2007 ; Amigud et al., 2017 ; Hall et al., 2021 ; Wuthisatian, 2020 ; Alessio et al., 2017 ), while others note concerns about increased test-taker anxiety and privacy violations (Hylton et al., 2014). Scholars consider AI for assessment transformative as it can potentially facilitate the provision of timely feedback and be integrated into learning activities for continuous analysis of a student’s learning progress (Zawacki-Richter et al., 2019 ). This study addresses a critical knowledge gap by exploring student and faculty perceptions of AIRPS in Ghanaian distance education programs through the theoretical lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). The research is particularly timely as Ghanaian universities seek sustainable approaches to remote assessment that balance academic integrity with the unique infrastructural challenges and socio-cultural contexts of their student populations. By using a concurrent mixed-method research approach as the methodological framework, the opinions of students and faculty on AIRPS in the distance education spaces of Ghana are explored. This study addresses three key research questions that emerge from the UTAUT framework. First, we examine whether demographic factors (gender, location, experience, and generation) moderate students' acceptance of AIRPS in Ghana. Second, we investigate how students and faculty perceive the performance expectancy(PE) and effort expectancy(EE) of AIRPS in distance education contexts in Ghana. Finally, we explore the role of social influence(SI) and facilitating conditions(FC) in shaping AIRPS acceptance in Ghana. 2.0 Literature Review 2.1: The Digital Divide in Online Assessment During the COVID-19 Pandemic At the peak of the COVID-19 pandemic when social distancing and lockdowns were enforced, online assessment became the only practical means of measuring students' progress and providing feedback (Dietrich et al., 2020 cited in Maboe et al., 2023). However, the adoption of online learning and assessment during COVID-19 was not without challenges. Joseph et al., (2022) report that the demand for digital capability for education and training led to challenges and inequalities between different groups of students and institutions according to their access to technology and digital competencies. In Europe and some countries in Asia such as China, India, and Japan, prompt support from companies made accessibility to different software and technological tools available for educators with the intent to address pressing challenges related to remote online assessment. Some of these solutions included AI-powered proctoring systems meant to ensure academic integrity in a remote online test environment. The use of digital technologies for assessment in African HEI is a relatively new concept. Before the COVID-19 pandemic, the traditional assessment forms (both formative and summative) were the "paper and pen" styled examination in a "brick and mortar" edifice. Human invigilators were usually used to ensure examination integrity and control cheating behavior. However, the sudden transformation and dependency on technology-mediated assessment during the pandemic required even the most resource-constrained HEIs in Sub-Sahara Africa to innovate by employing e-learning modes of delivery to facilitate teaching, learning, and assessment (Adotey, 2020 ). The COVID-19 pandemic's impact on educational technology adoption was paradoxical. Thus while accelerating digital transformation globally, it simultaneously exposed and widened existing digital divides. African institutions faced particular challenges, with only 29% successfully transitioning to online instruction compared to 85% in Europe (Mariononi et al., 2020). This disparity reflects broader issues of technological equity and access that continue to influence educational technology adoption in sub-Saharan Africa (Adotey, 2023; De los Santos et al., 2020) An Afro-barometer survey further reveals that only 1/5th of African adults are well-prepared to participate in an online learning environment with significant disparities across the various African countries (Krönke, 2020 ). The Composite Index of Remote Learning Readiness (CIRLR) is a scale that combines indicators of device ownership and digital literacy to measure the preparedness of African nationals to participate in or help with remote online learning. It shows that Mauritius, Cape Verde, and Morocco are among the top three(3) African countries with high preparedness for remote online learning while countries like Niger, Mali, Malawi, Madagascar, and Tanzania lag (Krönke, 2020 ). This apparent digital divide shows that concerted efforts from all stakeholders ought to be invested to deliberately promote remote learning readiness among African nations with the view to make tertiary education accessible to all. 2.2: Online Assessment Practices in Ghana during the COVID-19 pandemic Various online assessment forms were employed in Ghana during the pandemic (Sedofia and Ampadu, 2022 , Baidoo-Anu -Anu et al., 2023). Most of the assessment practices were instructor-specific involving a "trial and error" approach with deep concerns over its integrity due to the near absence of proctoring technologies in the country (Baboolal-Frank, 2021 cited in Baidoo-Anu et al., 2023 ). The most common forms of assessment during the period were online participation grades, group work presentations, randomized multiple-choice questions, random questions during Zoom lectures, "Hodgepodge" grading (marks for being present at a Zoom), and feedback to a selected few due to the large class size (Sedofia and Ampadu, 2022 , Appiah-Adjei 2021, Baidoo -Anu et al, 2023). Others included online discussion via WhatsApp Google Meets, telegram, or learning and content management systems, file uploads, fieldwork, and e-portfolio, each of which when not proctored effectively could lead to academic dishonesty. Ghana, like most African countries, made a significant transition to e-learning due to COVID-19 with milestone investment in LMS, webinars, and training targeted at e-learning adoption in HEI. Sedofia and Ampadu ( 2022 ) report that e-learning and e-assessment have always existed in the country but COVID-19 “made the entire university community take it seriously”. However, with major concerns over online assessment integrity due to impersonation and contract cheating, there exist challenges in the adoption of AIRPS in Ghana to improve authorship assurance and academic integrity for remote online assessment. Moreover, the pedagogical potential of technology-mediated learning and assessment is yet to be explored as systematic reviews show that African researchers are woefully lacking in the area of AI in education (Zawacki-Richter et al., 2019 ) 2.2 Artificial Intelligence Remote Proctoring Systems: Benefits and Challenges Online proctoring is the use of digital infrastructure (software or browser extensions) to monitor or supervise the test taker's assessment environment in order to ensure the credibility of an institution's examination processes (Hussein et al., 2020 ). With recent advancements in Artificial Intelligence in education, AI for remote proctoring has emerged as a surveillance solution that seeks to increase the legitimacy of online examinations by providing additional security features to combat academic dishonesty- a critical challenge in online examinations (Swauger, 2020 ). The Artificial Intelligence Remote Proctoring System (AIRPS) is an educational assessment innovation designed to ensure academic integrity in an online test environment. Test takers write the exam off-site in adherence to World Health Organisation (WHO) protocols on social gathering and movement during pandemic-like emergencies. The system uses audio-visual and acoustic surveillance technology currently embedded in modern-day laptops with biometric authentication. The proctor records the exam sessions using surveillance technology to monitor students and their test environment via a 360-degree webcam. Suspicious actions are flagged by the software and can later be referred to for further disciplinary actions (Bedford, Gregg, & Clinton, 2009 ). This module has been used by some prominent universities across Europe to ensure academic integrity, however, the same cannot be said of Ghanaian universities offering distance programs due to context-specific challenges such as technology access. According to Mpahalala et al., (2024), there is an unresolved debate regarding the adoption of online proctoring of examinations in HEI due to issues of credibility, reliability, and validity of the entire examination process. The arguments for AIRPS state that the system affirms academic integrity by providing a form of surveillance technology that curbs cheating behavior(Swauger, 2020 ). Such an understanding may be based on the Panopticon theory of security control on which AIRPS is modeled in which students internalize the idea of being watched (Foucault,1977). Zhakata (2024) calls this the "Big Brother" or "Cyber Spy" syndrome that may potentially create test-taker anxiety and invade students' privacy. Paredes et al., (2021) and Balash et al., ( 2021 ) report that the intrusive nature of AIRPS can potentially affect a student's achievement score and as such encourage research that balances the benefits over potential risks of AIRPS. Further arguments for AIRPS include prompt feedback on multiple choice questions, and providing support for equity, diversity, and inclusivity in the case of persons with disability who are unable to travel long distances to write their exams for fully online programs (Hussein et al., 2020 , Swauger, 2020 ). It is also cost-effective in comparison to the use of human proctors for remote assessment. More importantly, AIRPS allows for educational continuity in the event of a social disruption such as armed conflicts, pandemics, and natural hazards (issues not uncommon in Africa) that make the movement and gathering of students practically impossible. Notable challenges of AIRPS beside ethical concerns is its potential to foster a culture of discrimination and distrust between students and faculty. For instance, the eye tracking device of an AIRPS is said to be biased towards persons with neurological and visually impairment (Swauger, 2020 ). Additionally, a common test-taking behavior like reading a question aloud or writing it down through can be flagged as a cheating behavior (Ibid). These concerns require investment in AI algorithms that address the challenges identified by stakeholders of the system for e-proctoring. Scholarship in this field indicates that the next 'big thing' after e-learning is an online assessment and the implementation of AI-based proctoring systems has become the 'need of the hour' for distance education and emergency remote assessment (Nigam et al., 2021 , Hwang 2020 , Chen et al., 2020 ). The entire world currently hangs on technology-driven solutions and AI for online assessment cannot be left out of the equation in higher education studies. It is important to mention that the shift toward online assessment and e-proctoring is not merely a response to technological advancement but represents a fundamental transformation in how education is conducted (Terblanche et al., 2023 ) 2.2: Theoretical Framework The Unified Theory of Acceptance and Use of Technology(UTAUT) is a widely used framework to identify factors likely to predict factors that can influence the adoption of AI technology in remote online assessment in many contexts. The theoretical model of UTAUT suggests that the actual use of a technology is determined by behavioral intention. Thus the perceived likelihood of adopting an AIRPS is dependent on the direct effect of four key constructs, namely performance expectancy, effort expectancy, social influence, and facilitating conditions. The effect of these predictors is moderated by age, gender, and experience (Venkatesh et al., 2003 ). The constructs from the UTAUT are explained as follows in the context of the current study. Performance Expectancy (PE) is explained as the extent to which both students and lecturers believe that using the AIRPS can help them attain gains in their academic performance. Flexibility in the assessment process and prompt feedback delivery for the students and reduced workload on the part of the lecturers. Research suggests that PE is the strongest predictor of behavioral intention to use AIRPS because it combines constructs from several competing theories on technology acceptance, notable of which are the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Innovation Diffusion Theory (IDT) and it is significant in both voluntary and mandatory settings (Zhou, Lu & Wang, 2010 ; Venkatesh, Thong & Xu, 2016 ) Effort Expectancy (EE) is defined as "the degree of ease associated with the use of the system" (Venkatesh et al., 2003 ). Thus both students and lecturers are more likely to adopt an AIRPS if they find it to be user-friendly and easy to navigate. Social Influence (SI) , on the other hand, considers the impact of peers, educators, and institutional norms on students' decisions to use online proctoring systems. Positive endorsements from educators and the influence of peer acceptance play a significant role in shaping students' perceptions and decisions about the adoption of an AIRPS. The effect of SI is significant when the use of a new technology is mandatory or obligatory due to power dynamics held by the university management and the compliance order (Venkatesh et al., 2003 ). Facilitating Conditions (FI) refers to the availability of resources and support, such as technical assistance and necessary hardware. This significantly impacts the acceptance of online proctoring systems as students are more likely to adopt these systems when they have access to the required infrastructure and technical support. The moderating variables considered to likely influence any behavioral intention to use an AIRPS in the Ghanaian distance education space are a person's generation, previous online learning experience, gender, and location. It can be noted that "generation" and "location" are the factors added to Ventakesh's UTAUT model to explain behavioral intention to use an online remote proctoring system. This addition is premised on the fact that there are stereotypical notions in Africa that these factors including gender may determine technology acceptance. The UTAUT theory has been applied in studies across African Higher Institutions (HEI) to determine factors that can influence the adoption of proctored online examinations. In South Africa, Mutambara and Chibisa (2024) sought to identify the characteristics that influence students’ acceptance of online proctored assessments. Their findings revealed that “perceived usefulness”, “perceived attitude”, and “question quality” all had a direct impact on actual use of online proctoring technologies. On the other hand, “perceived social influence”, “perceived ease of use”, and “institutional support” had an indirect influence. Akindele et al., ( 2024 ) also investigated the key elements influencing the adoption and implementation of online proctored assessments by educational stakeholders in an open university in Nigeria. Their findings suggested that “Performance Expectancy” (0.27), “Social Influence” (0.177), and “Personal Innovativeness” (0.161) had the strongest positive relationship with “Behavioral Intention” to adopt remote proctored examination. 3.0 Methodological Approach 3.1: Research design This study employed a concurrent mixed-methods design, integrating both quantitative and qualitative data collection and analysis. This approach allowed for a comprehensive understanding of perceptions toward AI Remote Proctoring Systems (AIRPS) by triangulating numerical trends with contextual insights. The design aligns with Creswell and Creswell’s (2018) framework for mixed-methods research, ensuring both breadth and depth in data interpretation. 3.2 Setting and Participants 3.2.1 Institutional Selection We selected Kwame Nkrumah University of Science and Technology (KNUST) and University of Cape Coast (UCC) based on two criteria: (1) significant distance education enrollment (representing >60% of Ghana's distance education students; NCTE, 2015), and (2) established online learning infrastructures that could potentially support AIRPS implementation. 3.2.2 Participant Recruitment Quantitative Component: Using stratified random sampling, we recruited distance education students stratified by institution and academic level. The target sample size was calculated using G*Power 3.1.9.7 for multiple regression with a medium effect size (0.15), power of 0.80, alpha of 0.05, and seven predictors, yielding a minimum requirement of 103 participants. Qualitative Component: We used purposive sampling to select fifteen (15) faculty members with online examination experience. This ensured diversity across demographic characteristics and academic disciplines. 3.3 Data Collection Instruments 3.3.1 Survey Instrument We adapted the UTAUT questionnaire (Venkatesh et al., 2003) for the AIRPS context. The instrument included demographic items (8 questions) and UTAUT constructs measured on 5-point Likert scales. The constructs were Performance Expectancy (4 items, α = 0.87), Effort Expectancy (4 items, α = 0.82), Social Influence (3 items, α = 0.79), Facilitating Conditions (4 items, α = 0.81) and Behavioral Intention (3 items, α = 0.85) 3.3.2 Interview Protocols The interview protocols were semi-structured, with questions aligned to UTAUT constructs. Sample questions included: "As a faculty member, what benefits do you perceive in using AI-based proctoring for examinations?" and "What challenges would you anticipate if your institution implemented AIRPS?" 3.4 Data Collection Procedures Data collection occurred from March to April 2024. Surveys were distributed via Google Forms on students’ community platforms and chat groups. Interviews were conducted via Zoom, recorded with consent, and transcribed verbatim. 3.5 Data Analysis 3.5.1 Quantitative Analysis We used Jamovi 2.5.3 for the analysis. After checking assumptions, we employed descriptive statistics for participant characteristics, Mann-Whitney U and Kruskal-Wallis tests for group comparisons (due to non-normal distributions), and multiple linear regression to examine relationships between the UTAUT constructs and behavioral intention to use the AIRPS. 3.5.2 Qualitative Analysis We conducted thematic analysis following Braun and Clarke's (2006) six-phase process, using UTAUT constructs as initial codes while remaining open to emergent themes. 3.6 Scenario Development To aid understanding of AIRPS, participants were presented with a scenario-based vignette describing a typical remote proctored exam using AI. The vignette included features such as webcam monitoring, biometric authentication, and automated flagging. It was validated by three educational technology experts for clarity and realism. The purpose of the scenario-based approach was to offset the technological deficit and to stimulate dialogue among students and faculty on the potential factors that can influence the adoption of remote e-proctoring systems in the Ghanaian distance education space (Bai et al., 2022). 3.7 Reliability and Validity The survey instrument was piloted with ten postgraduate students for clarity and comprehensiveness. The Cronbach's alpha for the overall instrument was 0.839, indicating good internal consistency. For qualitative data, trustworthiness was established through member checking, peer debriefing, and maintaining an audit trail. 3.8 Ethical Considerations Ethical approval was obtained from the KNUST Human and Social Science Research Ethics Committee (HUSSREC) in March 2024. All participants provided informed consent. Data were anonymized, and participation was voluntary with the right to withdraw at any time. 4.0 Results 4.1: Respondent Characteristics A total of 370 valid responses were collected from the study population. Of these, a majority of participants (n = 282, 76%) were affiliated with the Institute of Distance Learning at the Kwame Nkrumah University of Science and Technology (IDL-KNUST), while 49 respondents (13.2%) were from the College of Distance Education at the University of Cape Coast (CoDE-UCC) (Table 1 ). Regarding educational level, 233 respondents (63%) were enrolled in undergraduate programs, and 135 (36.5%) were pursuing postgraduate studies. Participants’ fields of study included development studies, industrial finance and investment, engineering, nursing, psychology, human resource management, geography and rural development, commerce, procurement, and supply chain management. In terms of gender distribution, 237 respondents (64.1%) identified as male, and 133 (35.9%) as female. Digital literacy was notably high, with 360 respondents (97%) reporting good to excellent proficiency in the use of computers and internet tools for academic purposes. Additionally, 343 participants (92.7%) had prior experience with online assessments. Table 1 Demographic Profile of Respondents Institutional Affiliation Level Count Percentage IDL-KNUST Under-graduate 166 46.0% Post-graduate 116 32.1 others 1 0.3 CoDE-UCC Under-graduate 34 9.4 Post-graduate 15 4.2 others 0 0.0 Others Under-graduate 28 7.7 Post-graduate 1 0.3 Total 361 97.8% Gender Count Percentage Male 237 64.1% Female 133 35.7% Total 370 100% Generation Count Percentage Gen Z 77 20.8% Millenials 257 69.5% Gen X 36 9.7% Total 370 100% Location Count Percentage Urban 258 69.7% Peri-Urban 60 16.2% Rural 52 14.1% Total 370 100% Digital Literacy Count Percentage Poor 2 0.5 Very poor 8 2.7 Good 151 43.5 Very Good 137 80.5 Excellent 72 19.5 Total 370 100% Online Assessment Experience Count Percentage Yes 343 92.7% No 27 7.3% Total 370 100% Preferences regarding examination formats were distributed as follows: 169 respondents (45.8%) preferred online examinations administered in a centralized location, 44 (11.9%) favored remotely proctored online examinations, and 156 (42.3%) preferred traditional in-person examinations with human invigilators. With respect to generational categorization, 257 respondents (69.5%) identified as Millennials, 77 (20.8%) as members of Generation Z, and 36 (9.7%) as Generation X. While Millennials and Generation Z are typically described as “digital natives,” Generation X is often referred to as “digital immigrants.” Nonetheless, all generational cohorts reported familiarity and comfort with the use of digital technologies for academic tasks. In terms of residential location, most respondents (n = 258, 69.7%) resided in urban areas, followed by 60 (16.2%) in peri-urban areas, and 52 (14.1%) in rural communities. 4.2 Research Question 1: Influence of Demographic Factors on AIRPS Acceptance The gender dimension in technology acceptance is an important element of the original UTAUT theory and may potentially moderate students acceptance of AIRPS (Ventakesh et al, 2003). A Mann-Whitney U test revealed no statistically significant difference in AIRPS behavioral intention between male (Mdn = 3.67, IQR = 1.33) and female (Mdn = 3.83, IQR = 1.17) participants, U = 15,048, p = 0.467, ε² = 0.045, 95% CI for effect size [0.001, 0.089](Table 2 ). The observed effect size (ε² = 0.045) falls within Cohen's (1988) small effect range but represents only 4.5% of variance explained. This suggests that gender-based customization of AIRPS implementation strategies may not be necessary. This supports a gender-neutral approach to technology deployment within the study’s context. However, the 95% confidence interval for the effect size ranges from near-zero to small-moderate (0.089), indicating some uncertainty about the true magnitude of gender differences. Statistical Power Considerations Post-hoc power analysis indicated 67% power to detect medium effects (d = 0.5) with the current sample size, suggesting adequate sensitivity for practically meaningful differences.. This finding is consistent with earlier research by the Institute of Chartered Accountants, Ghana (2023) in which the acceptance rate of AI proctoring solutions for remote online exams was not hinged on gender. This can be explained by the relatively high digital literacy rate among the study sample (Table 1 ) A Kruskal-Wallis test between generation and behavioral intention (BI) was conducted to determine if a student's generational identity can significantly influence his/her intention to use the e-proctoring system for online assessment (Table 2 ). The test revealed no statistically significant difference in behavioral intention to use AIRPS across the different student generations (χ² = 0.471, p = 0.790). The very low effect size (ε² = 0.00128, 95% CI [0.000, 0.008]) indicates that generational identity accounts for less than 0.13% of the variance in behavioral intention to use AIRPS. According to Cohen's (1988) guidelines, this represents a negligible practical effect. From an implementation perspective, this finding suggests that universities can develop unified AIRPS training and support programs without age-specific modifications. This can potentially reduce implementation costs and complexity. However, the small effect size should be interpreted alongside the study's statistical power (1-β = 0.52 for small effects), indicating limited ability to detect small but potentially meaningful differences between generational groups.These findings contradict stereotypical notions about generational adaptations to technology and confirm conclusions that the generational gap in technology acceptance in education seems to be narrowing (Bennet,2023). Several adaptations to the UTAUT theory show that location and experience can be significant predictors of behavioral intentions to use a given educational technology (Venkatesh et al., 2003 ). For instance, Chibisa and Mutambara ( 2023 ) argue that rural university students in South Africa are in favor of the continued use of proctored examinations likely due to its flexibility and adaptability. However, the p-values of location and previous online examination experience of students in this study were 0.061 and 0.0439 respectively (Table 2 ). Their very low effect sizes (ε² = 0.00953 and 0.00447) show that neither of these factors can determine the adoption of AIRPS within the distance education spaces among the study sample. Table 2 Gender/Generation/Experience/Location and Behavioral Intention to Adopt AIRPS Statistic df p ε² Gender and BI Student’s t −0.868 368 0.386 0.0452 Mann-Whitney U 15048 0.467 χ² df p ε² Generation and BI 0.471 2 0.790 0.00128 Experience and BI 3.52 1 0.061 0.00953 Location and BI 1.65 2 0.439 0.00447 4.3: Research Question 2: Perception of students and faculty on Performance Expectancy(PE) and Effort Expectancy(EE) of AIRPS for online assessment in Ghana. Table 3 Model Coefficients - BI Predictor Estimate SE t p Intercept 0.4628 0.1551 2.983 0.003 PE 0.3621 0.0614 5.901 < .001 EE 0.3088 0.0676 4.567 < .001 SI 0.1698 0.0640 2.653 0.008 FC 0.0417 0.0567 0.735 0.463 Dependent Variable = Behavioral Intention (BI) A multivariate regression analysis results indicate that Performance Expectancy (PE) had the strongest standardized effect (ẞ = 0.3621, p < .001)(Table 3 ). This suggests that respondents who believe the AIRPS system is beneficial in completing their examinations are significantly more likely to adopt it. This finding is consistent with Akindele et al., ( 2024 ) research in Nigeria in which PE (0.27) had the strongest positive relationship with behavioral intention to adopt remote proctored examinations. A thematic analysis of the qualitative interviews suggests that some faculty members prioritize the potential of AIRPS to boost their productivity amidst safety concerns. A professor with over 20 years experience as a supervisor in the conduct of distance examinations noted : "... the current examination style of moving human proctors across the country to conduct examinations for students is risky and unsustainable. Imagine, in case of an accident… "(which God forbid! )", all these valuable human resources of the nation will be lost. E-assessment is actually what should be done by the distance education units of our universities, where students should be allowed to write from whichever location they prefer…its all about adaptability to suit our local context…after all, there are many remote assessments like the GRE that have high integrity. It's all about setting our priorities right…"(April, 2024) The findings on PE are revealing but inconclusive of faculty's perception of remote proctored examination. For instance, a faculty member with a contradictory viewpoint said: "As a lecturer, I will not use online examinations in my course because of what I have seen students do during such an assessment. The students are knowledgeable in the use of computers to cheat and doubt if the e-proctoring systems can be an adequate measure to ensure the integrity of the test"(Exams officer and invigilator_2024) Effort Expectancy (EE) also emerged as a significant predictor (ẞ = 0.3088, p < .001) of students adoption intention of AIRPS. The significant and positive relationship between EE and BI supports another core assumption of the UTAUT theory—that the easier a system is to use, the more likely individuals are to adopt it (Ventakesh et al., 2003). These findings contradict research by Jiang et al., ( 2023 ) in which EE did not significantly and positively affect online proctoring acceptance. They explained that familiarity with the technical requirement of online proctoring technologies may have accounted for the results in their context. However, in Ghana, AIRPS is not a familiar proctoring tool, and as such its perceived ease of use is important to the research participants. 4.4: Research Question 3: The role of Social Influence(SI) and Facilitating Conditions(FC) in shaping AIRPS acceptance in Ghana. Social Influence (SI) was another significant predictor of AIRPS in the study sample but not the strongest (ẞ = 0.1698, p = 0.008). This finding suggests that students' decision to use online proctoring technology is largely dependent on the positive endorsement of AIRPS by university management and regulatory bodies such as the Ghana Tertiary Education Council (GTEC). These results agree with earlier research in Nigeria in which positive peer endorsement was a critical factor in the acceptance of online proctoring technology among distance students (Akindele et al., 2024 ). A faculty member highlighted that, "...technology is changing the face of assessment and we cannot afford to do things the usual way, if university management says that e-proctoring solution is the new way of online assessment, why not? We (lecturers) will support it" (Lecturer and examiner_2024) Interestingly, Facilitating Conditions (FC) did not significantly predict behavioral intention to use the AIRPS among the study participants (ẞ = 0.0417, p = 0.463). This result contradicts Bayaga and Plessis ( 2023 ) who stated that FC is a significant predictor of BI to adopt and use Learning Management System(LMS) in developing countries. The results also challenge popular assumptions that students' concern over access to technological resources or support infrastructure may impact their intention to use AIRPS. The qualitative findings revealed that some distance education units in Ghana have invested heavily in infrastructures that encourage online examination for distance learners. One faculty states, "online assessment is a "game-changer" in how we assess our students and our institution is working towards that by establishing computer labs across all our learning centers...this is because COVID taught us a valuable lesson in education services delivery…cheating can be resolved by freezing the computers of the test-taker" (examiner and board chairman, online assessment committee, 2024 ) 4.4.1 Model Fit Table 4 Model Fit Measures Overall Model Test Model R R² Adjusted R² F df1 df2 p 1 0.691 0.478 0.472 83.6 4 365 < .001 The model, based on the constructs of the UTAUT theory to determine behavioral intention to adopt AIRPS in distance education in Ghana demonstrated a multiple correlation coefficient (R) of 0.691. This indicates a moderately strong linear relationship between the predictors and the dependent variable. More importantly, the coefficient of determination (R²) was 0.478, which means that approximately 47.8% of the variance in behavioral intention can be explained by the four predictors in the model (Table 3 ). This suggests a moderate level of explanatory power, indicating that these variables collectively offer substantial insight into what drives users' intentions to engage with the AIRPS system. The F-statistic (F = 83.6, p < .001) confirms the model's overall statistical significance, rejecting the null hypothesis that all coefficients are zero. This underscores that the predictors collectively contribute meaningfully to understanding AIRPS adoption in Ghanaian distance education. The regression model further tested a wide range of demographic variables (e.g., gender, generation, previous experience, and location) and their interaction effects on to use of AIRPS. Interestingly, as earlier confirmed from the non-parametric T-Tests, none of these demographic variables nor their interactions had statistically significant effects on behavioral intention. For example, differences between gender groups, generational cohorts (Gen Z, Millennials, Gen X), urban versus rural users, and previous experience with similar systems did not predict changes in BI. Interaction terms—such as gender × generation, generation × location, or even three-way and four-way interactions—also failed to reach statistical relevance. 5.0 Discussion This study investigated perceptions of AI Remote Proctoring Systems among students and faculty in Ghanaian distance education programs through the lens of the UTAUT model. The study provides insight into the factors influencing AIRPS acceptance in this context and challenges some common assumptions about technology adoption in Global South settings where gender, location, generation, and previous online learning experience are assumed to play a key role in technology acceptance in education (Bayaga and Plessis, 2023 ). 5.1 Demographic Factors and AIRPS Acceptance Contrary to prevailing assumptions about digital divides in sub-Saharan Africa, the study’s findings demonstrate that demographic factors—gender, generation, location, and prior experience—did not significantly influence behavioral intention to use AIRPS. This contradicts previous studies that found these factors to be important moderators of technology acceptance (Venkatesh et al., 2003 ; Richardson & Clesham, 2021 ). The sample suggests that digital literacy may be more widespread across demographic groups than previously assumed, particularly among those enrolled in distance education programs who already navigate digital learning environments. The absence of generational differences is particularly noteworthy, as it challenges general stereotypes that older individuals (Gen X) would be less receptive to advanced technologies than their younger counterparts (Gen Z and Millennials). This finding aligns with emerging research suggesting that generational technology gaps may be narrowing in educational contexts (Bennett, 2023 ), and indicates that implementation strategies need not be heavily differentiated by age group. 5.2 Key Predictors of AIRPS Acceptance Performance Expectancy emerged as the strongest predictor of behavioral intention to use AIRPS, confirming findings from previous UTAUT studies in educational technology contexts (Akindele et al., 2024 ). This suggests that emphasizing the benefits of AIRPS—such as flexibility, integrity, and efficiency would be crucial for successful implementation in Ghanaian universities. As one faculty participant noted, traditional assessment methods involving human proctors traveling across the country are "risky and unsustainable," highlighting the practical advantages of remote solutions. The significant influence of Effort Expectancy indicates that perceived ease of use remains a critical factor for technology adoption, particularly in contexts where advanced technological systems are less common. This aligns with Davis' (1989) original Technology Acceptance Model and suggests that simple, intuitive interfaces would be essential for AIRPS implementation in Ghanaian distance education. The significant role of Social Influence reflects the importance of institutional endorsement and peer acceptance in technology adoption decisions. This finding supports Venkatesh et al.'s ( 2003 ) assertion that social factors are particularly influential in mandatory adoption contexts, as would likely be the case with institutional implementation of AIRPS. Surprisingly, Facilitating Conditions did not significantly predict behavioral intention to use AIRPS, a finding that seems contrary to studies by Bayaga and Plessis ( 2023 ) on students' acceptance of e-learning systems. This may reflect the high digital literacy of our sample or suggest that participants assumed necessary infrastructure would be provided by their institutions. Alternatively, it may indicate that concerns about infrastructure are overshadowed by perceptions of usefulness and usability. In summary, these findings suggest that the influence of PE, EE, and SI on behavioral intention is consistent across different demographic groups, reinforcing the idea that perceptions of usefulness, ease of use, and social encouragement are universal factors in the adoption of AIRPS in case institutions. The lack of significant moderation implies that AIRPS implementation strategies may not need to be heavily tailored by user demographics but should instead focus on improving perceived system utility, ease, and endorsement. 5.3 Practical Implications for Implementation Our findings suggest several practical implications for universities considering AIRPS implementation in Ghanaian distance education programs: First, implementation efforts should prioritize demonstrating system benefits (addressing Performance Expectancy) and ensuring user-friendly interfaces (addressing Effort Expectancy). Training programs should emphasize how AIRPS can enhance assessment flexibility while maintaining integrity. Secondly, institutional leadership should actively endorse and promote AIRPS adoption (leveraging Social Influence), providing clear policies and support for both students and faculty during transition periods. Thirdly, despite the non-significance of Facilitating Conditions in our model, institutions should still ensure adequate technical infrastructure and support, particularly given the known challenges with internet connectivity in some regions of Ghana. Finally, implementation strategies need not be heavily differentiated by demographic factors, allowing for more streamlined, universal approaches to training and support. 5.4: Theoretical Implication The findings extend the UTAUT model by demonstrating its applicability in a low-resource, post-pandemic African context. The non-significance of demographic moderators suggest that digital readiness may be more evenly distributed among distance learners than previously assumed. This challenges traditional views of the digital divide and supports calls for more context-sensitive models of technology acceptance in education. 5.5 Limitations and Future Research This study has several limitations that suggest directions for future research. First, our use of scenario-based vignettes may not fully capture how participants would respond to actual system usage. Future studies should examine perceptions during or after actual implementation experiences. Secondly, our sample was limited to two universities and may not represent all Ghanaian distance education programs. Future research should expand to a wider range of institutions, including private universities and those in more rural regions. Thirdly, generalization of the study's findings should be done with caution as the sample did not meet normality assumptions justifying the use of non-parametric tests. Additionally, the reliance on self-reported data introduces potential social desirability bias. Finally, future research should explore additional factors beyond the UTAUT framework that may influence AIRPS acceptance in African contexts, such as privacy concerns, cultural attitudes toward surveillance, and institutional trust. 6.0 Conclusion This study contributes to understanding technology acceptance factors for AI Remote Proctoring Systems in Ghanaian distance education, addressing a significant gap in the literature on educational technology implementation in sub-Saharan African contexts. Our findings challenge some prevailing assumptions about demographic influences on technology adoption and confirm the primary importance of perceived usefulness, ease of use, and social influence in predicting acceptance intentions. For Ghanaian universities navigating the post-COVID landscape of distance education, our results suggest that successful AIRPS implementation depends primarily on demonstrating system benefits, ensuring user-friendly design, and securing institutional endorsement rather than tailoring approaches to specific demographic groups. As online assessment continues to evolve globally, this research provides valuable insights into how advanced proctoring technologies might be effectively integrated into educational systems with unique infrastructural and cultural contexts. While technological solutions like AIRPS offer promising approaches to assessment integrity challenges, their successful implementation ultimately depends on alignment with local educational needs, values, and resources. Future policy and practice in Ghanaian distance education should therefore approach AIRPS adoption as part of a broader strategy for enhancing educational quality and accessibility while respecting the unique characteristics of the Ghanaian educational landscape. Declarations Availability of data and materials : All data generated and analyzed during the study are included in this published article. Funding : Not Applicable Acknowledgment : Our sincere appreciation goes to Stephen Bandoma for his invaluable comments, professional proofreading and referencing support. References Adotey, S. K. (2020). What will higher education in Africa look like after COVID-19? World Economic Forum. Accessed 02.02.2025 higher-education-africa-covid19-coronavirusdigital-online/ Alessio, H. M., Malay, N., Maurer, K., Bailer, A. J., & Rubin, B. (2017). Examining the effect of proctoring on online test scores. Online Learning , 21 (1), 146-161. Akindele, A. T., Adeoye, O. O., Arulogun, O. T., Osunniyi, J. S., Akanbi, R. Y., & Oluwatobi, M. F. (2024). Stakeholder’s perception on remote proctored exam in higher educational institutions: insights from lautech open and distance learning centre. Multidisciplinary Journal of Distance Education Studies , 2 (1). Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. Higher education studies , 10 (3), 16-25. Amigud, A., Arnedo-Moreno, J., Daradoumis, T., & Guerrero-Roldan, A. E. (2017). Using learning analytics for preserving academic integrity. International Review of Research in Open and Distributed Learning , 18 (5), 192-210. Appiah-Adjei, G. (2022). COVID-19 and e-assessments in Ghanaian higher education institutions: The case of journalism education. In Teaching and learning with digital technologies in higher education institutions in Africa (pp. 120-144). Routledge. Baboolal-Frank, R. (2021). Emergency remote learning during the pandemic from a South African perspective. International Journal for Educational Integrity , 17 (1), 22. Bai, J. Y., Zawacki-Richter, O., & Muskens, W. (2022, September). Developing strategic scenarios for artificial intelligence applications in higher education. In 5th International Open and Distance Learning Conference Proceedings Book (pp. 47-70). Baidoo-Anu, D., Asamoah, D., Quainoo, E. A., Gyamerah, K., Amoateng, E. Y., & Sasu, E. O. (2023, October). Emergency remote assessment practices in higher education in sub-Saharan Africa during COVID-19. In Frontiers in Education (Vol. 8, p. 1221115). Frontiers Media SA. Balash, D. G., Kim, D., Shaibekova, D., Fainchtein, R. A., Sherr, M., & Aviv, A. J. (2021). Examining the examiners: Students' privacy and security perceptions of online proctoring services. In Seventeenth symposium on usable privacy and security (SOUPS 2021) (pp. 633-652). Bayaga, A., & du Plessis, A. (2023). Ramifications of the Unified Theory of Acceptance and Use of Technology (UTAUT) among developing countries’ higher education staffs. Education and Information Technologies , 29 (8), 9689-9714. Bedford, W., Gregg, J., & Clinton, S. (2009). Implementing technology to prevent online cheating: A case study at a small southern regional university (SSRU). MERLOT Journal of Online Learning and Teaching , 5 (2), 230-238. Bennett, R. E. (2023). Toward a theory of socioculturally responsive assessment. Educational Assessment , 28 (2), 83-104. Bloemers, W., Oud, A., & Dam, K. V. (2016). Cheating on unproctored internet intelligence tests: Strategies and effects. Personnel Assessment and Decisions , 2 (1), 3. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology , 3 (2), 77-101. Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence , 1 , 100002. Chibisa, a., & Mutambara, D. (2023). Rural university students’continuous use of online proctored examinations. Journal of Theoretical and Applied Information Technology , 101 (15). Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches . Sage publications. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly , 319-340. Dietrich, N., Kentheswaran, K., Ahmadi, A., Teychené, J., Bessière, Y., Alfenore, S., ... & Hébrard, G. (2020). Attempts, successes, and failures of distance learning in the time of COVID-19. Journal of Chemical Education , 97 (9), 2448-2457. Foucault, M., & Deleuze, G. (1977). Intellectuals and power. Language, counter-memory, practice: Selected essays and interviews , 205 , 209. Gamage, K. A., Silva, E. K. D., & Gunawardhana, N. (2020). Online delivery and assessment during COVID-19: Safeguarding academic integrity. Education Sciences , 10 (11), 301. Ghana Tertiary Education Commission (GTEC), 2023. Distance Education Policy Audit: Survey among Ghana’s Tertiary Education Institutions. Committee on Partnership for Enhanced and Blended Learning (PEBL), August 2023. [online] Available at: > [Accessed 14.03. 2024]. Hall, E. A., Spivey, C., Kendrex, H., & Havrda, D. E. (2021). Effects of remote proctoring on composite examination performance among doctor of pharmacy students. American Journal of Pharmaceutical Education , 85 (8), 8410. Hébert, C. (2021). Online remote proctoring software in the neoliberal institution: measurement, accountability, and testing culture. in education , 27 (1), 23-40. Hwang, G. J. (2020). E-learning and innovative education: Strategies for adding innovation and value to educational research. Innovating Education in Technology-Supported Environments , 109-115. Hussein, M. J., Yusuf, J., Deb, A. S., Fong, L., & Naidu, S. (2020). An evaluation of online proctoring tools. Open Praxis , 12 (4), 509-525. Hylton, K., Levy, Y., & Dringus, L. P. (2016). Utilizing webcam-based proctoring to deter misconduct in online exams. Computers & Education , 92 , 53-63. Jiang, X., Goh, T. T., Chen, X., Liu, M., & Yang, B. (2023). Investigating university students’ online proctoring acceptance during COVID-19: An extension of the technology acceptance model. Australasian Journal of Educational Technology , 39 (2), 47-64. Joseph, D., Nethsinghe, R., & Cabedo-Mas, A. (2021). Online teaching and learning during COVID-19: flexible harmonies in Higher Education. In Online teaching and learning in higher education during COVID-19 (pp. 50-68). Routledge. Jurado de Los Santos, P., Moreno-Guerrero, A. J., Marín-Marín, J. A., & Soler Costa, R. (2020). The term equity in education: A literature review with scientific mapping in web of science. International Journal of Environmental Research and Public Health , 17 (10), 3526. Karim, M. N., Kaminsky, S. E., & Behrend, T. S. (2014). Cheating, reactions, and performance in remotely proctored testing: An exploratory experimental study. Journal of Business and Psychology , 29 , 555-572. Kitahara, R. T., & Westfall, F. (2007). Promoting academic integrity in online distance learning courses. MERLOT Journal of Online Learning and Teaching , 3 (3), 265-276. Krönke, M. (2020). Africa's digital divide and the promise of e-learning . Accra: Afrobarometer. Maboe, K. A., & Tomas, N. (2023). Online assessments and COVID-19: A qualitative study of undergraduate nursing students in Southern Africa. International Journal of Africa Nursing Sciences , 19 , 100590. Maphalala, M. C., Mpofu, N., & Adigun, O. T. (2024). The dynamics of e-assessment in South African Higher Education: Narratives for and against proctoring in higher education during COVID-19 and beyond. In Online teaching and learning in higher education: Issues and challenges in an African context (pp. 183-199). Cham: Springer Nature Switzerland. Marinoni, G., Van’t Land, H., & Jensen, T. (2020). The impact of Covid-19 on higher education around the world. IAU global survey report , 23 (1), 1-17. National Council for Tertiary Education (2015). Statistical Digest 2014/2015. Accra: NCTE. Nigam, A., Pasricha, R., Singh, T., & Churi, P. (2021). A systematic review on AI-based proctoring systems: Past, present and future. Education and Information Technologies , 26 (5), 6421-6445. Pokhrel, S., & Chhetri, R. (2021). A literature review on the impact of the COVID-19 pandemic on teaching and learning. Higher education for the future , 8 (1), 133-141. Richardson, M., & Clesham, R. (2021). Rise of the machines? The evolving role of Artificial Intelligence (AI) technologies in high-stakes assessment. London Review of Education , 19 (1), 1-13. Sedofia, J., & Ampadu, E. (2022). Assessment in higher education in Ghana amid the COVID-19 pandemic: Charting the challenges and paradoxes. In Teaching and Learning with Digital Technologies in Higher Education Institutions in Africa (pp. 90-105). Swauger, S. (2020). Our bodies encoded: Algorithmic test proctoring in higher education. Critical digital pedagogy . Terblanche, W., Lubbe, I., Papageorgiou, E., & van der Merwe, N. (2023). Acceptance of e-learning applications by accounting students in an online learning environment at residential universities. South African Journal of Accounting Research , 37 (1), 35-61. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly , 425-478. Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems , 17 (5), 328-376. Wuthisatian, R. (2020). Student exam performance in different proctored environments: Evidence from an online economics course. International Review of Economics Education , 35 , 100196. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). A systematic review of research on artificial intelligence applications in higher education–where are the educators? International journal of educational technology in higher education , 16 (1), 1-27. Zhakata, N. (2023). Retooling online proctoring technology in the Fourth Industrial Revolution learning contexts—from Big Brother to Learning Buddy. In Academic Quality and Integrity in the New Higher Education Digital Environment (pp. 117-130). Chandos Publishing. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior , 26 (4), 760-767. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6924498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473189910,"identity":"3a2108b5-67cb-4edd-94ee-61e59718a515","order_by":0,"name":"Sherry Adomah Bempah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACCTB5AESwMXwAkeykaGGcASKZSdHCzAOiCGmRn9187HNl2x053dnNzx7b/Nomz8fMwPjhYw5uLQZ3jiXPPNv2zNjszjFz49y+24ZtzAzMkjO34dEikWPM2Nh2OHHbjQQz6dye24xALWzMvHi0yM/I/wzSUr/tRvo3acue2/YEtTDcyGEGaUkwu5FjJs3w43YiQS0GN9KMGRvOPTPcdudMmWRvw+3kNmbGZrx+kZ+R/JixoeyOvNnt9m0SP/7ctp3f3nzww0d8DgMBRjYGSAQxtoG5DQTUg8AfqBYwYxSMglEwCkYBGgAA+qxVXPvJeM0AAAAASUVORK5CYII=","orcid":"","institution":"Kwame Nkrumah University of Science and Technology,Department of Teacher EducationKumasi-Ghana","correspondingAuthor":true,"prefix":"","firstName":"Sherry","middleName":"Adomah","lastName":"Bempah","suffix":""}],"badges":[],"createdAt":"2025-06-18 15:33:43","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6924498/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6924498/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84954616,"identity":"a3819018-5871-4e13-a0ec-335cd5bf38fa","added_by":"auto","created_at":"2025-06-19 08:04:58","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":436638,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6924498/v1/b410a6ef0d4282dab2c5984e.jpeg"},{"id":84956537,"identity":"48842c52-6bfc-4ea5-87d0-585887f65b82","added_by":"auto","created_at":"2025-06-19 08:20:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1735043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6924498/v1/8a18cbaa-fc3e-4804-b243-5d06706b72c2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFactors Influencing AI Remote Proctoring Acceptance in Ghanaian Distance Education: A Mixed-Methods UTAUT Analysis\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has fundamentally transformed the delivery of higher education worldwide, compelling institutions to rapidly migrate courses online to ensure educational continuity (Pokhrel \u0026amp; Chhetri, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While this digital pivot accelerated the global adoption of e-learning, it simultaneously highlighted significant disparities between regions. In Europe, approximately 85% of higher education institutions (HEIs) successfully transitioned to online instruction, compared to only 29% in Africa (Mariononi et al., 2020). This digital divide extends beyond course delivery to assessment practices, where ensuring academic integrity in remote settings presents particular challenges for institutions with limited technological infrastructure.\u003c/p\u003e \u003cp\u003eIn Ghana, universities offering distance education programs have experimented with various online assessment approaches during and after the pandemic (Sedofia \u0026amp; Ampadu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Baidoo-Anu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the adoption of sophisticated technologies like Artificial Intelligence Remote Proctoring Systems (AIRPS) remains limited. The Ghana Tertiary Education Council's recent survey reveals that AI-based proctoring remains largely unexplored in Ghana, with only two institutions currently implementing such solutions within their learning management systems (GTEC, 2023).\u003c/p\u003e \u003cp\u003eAIRPS represents an educational assessment innovation designed to ensure academic integrity in online examination environments. The system utilizes audio-visual surveillance technology and biometric authentication to monitor test-takers and their surrounding environment through webcams, flagging suspicious activities for review (Bedford et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Research on AIRPS implementation has grown in Western contexts, yielding diverse reactions regarding its effectiveness and acceptance (Hylton et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hall et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, little is known about how these systems are perceived within the Ghanaian higher education landscape, particularly in distance education settings.\u003c/p\u003e \u003cp\u003eIn the post-pandemic educational landscape, online assessments are expected to persist, with Artificial Intelligence Remote Proctoring Systems (AIRPS) gaining attention among higher education institutions (HEIs) worldwide (Swauger,2020). Some studies highlight AIRPS\u0026rsquo;s potential to enhance assessment integrity and invigilation (Bedford et al., 2011; Boyd et al., 2016; Hylton et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Karim et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bloemers et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Berhane et al., 2018; Kitahara \u0026amp; Westfall, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Amigud et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hall et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wuthisatian, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Alessio et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while others note concerns about increased test-taker anxiety and privacy violations (Hylton et al., 2014).\u003c/p\u003e \u003cp\u003eScholars consider AI for assessment transformative as it can potentially facilitate the provision of timely feedback and be integrated into learning activities for continuous analysis of a student\u0026rsquo;s learning progress (Zawacki-Richter et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This study addresses a critical knowledge gap by exploring student and faculty perceptions of AIRPS in Ghanaian distance education programs through the theoretical lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). The research is particularly timely as Ghanaian universities seek sustainable approaches to remote assessment that balance academic integrity with the unique infrastructural challenges and socio-cultural contexts of their student populations. By using a concurrent mixed-method research approach as the methodological framework, the opinions of students and faculty on AIRPS in the distance education spaces of Ghana are explored.\u003c/p\u003e \u003cp\u003eThis study addresses three key research questions that emerge from the UTAUT framework. First, we examine whether demographic factors (gender, location, experience, and generation) moderate students' acceptance of AIRPS in Ghana. Second, we investigate how students and faculty perceive the performance expectancy(PE) and effort expectancy(EE) of AIRPS in distance education contexts in Ghana. Finally, we explore the role of social influence(SI) and facilitating conditions(FC) in shaping AIRPS acceptance in Ghana.\u003c/p\u003e"},{"header":"2.0 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1: The Digital Divide in Online Assessment During the COVID-19 Pandemic\u003c/h2\u003e \u003cp\u003eAt the peak of the COVID-19 pandemic when social distancing and lockdowns were enforced, online assessment became the only practical means of measuring students' progress and providing feedback (Dietrich et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e cited in Maboe et al., 2023). However, the adoption of online learning and assessment during COVID-19 was not without challenges. Joseph et al., (2022) report that the demand for digital capability for education and training led to challenges and inequalities between different groups of students and institutions according to their access to technology and digital competencies. In Europe and some countries in Asia such as China, India, and Japan, prompt support from companies made accessibility to different software and technological tools available for educators with the intent to address pressing challenges related to remote online assessment. Some of these solutions included AI-powered proctoring systems meant to ensure academic integrity in a remote online test environment.\u003c/p\u003e \u003cp\u003eThe use of digital technologies for assessment in African HEI is a relatively new concept. Before the COVID-19 pandemic, the traditional assessment forms (both formative and summative) were the \"paper and pen\" styled examination in a \"brick and mortar\" edifice. Human invigilators were usually used to ensure examination integrity and control cheating behavior. However, the sudden transformation and dependency on technology-mediated assessment during the pandemic required even the most resource-constrained HEIs in Sub-Sahara Africa to innovate by employing e-learning modes of delivery to facilitate teaching, learning, and assessment (Adotey, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic's impact on educational technology adoption was paradoxical. Thus while accelerating digital transformation globally, it simultaneously exposed and widened existing digital divides. African institutions faced particular challenges, with only 29% successfully transitioning to online instruction compared to 85% in Europe (Mariononi et al., 2020). This disparity reflects broader issues of technological equity and access that continue to influence educational technology adoption in sub-Saharan Africa (Adotey, 2023; De los Santos et al., 2020)\u003c/p\u003e \u003cp\u003eAn Afro-barometer survey further reveals that only 1/5th of African adults are well-prepared to participate in an online learning environment with significant disparities across the various African countries (Kr\u0026ouml;nke, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Composite Index of Remote Learning Readiness (CIRLR) is a scale that combines indicators of device ownership and digital literacy to measure the preparedness of African nationals to participate in or help with remote online learning. It shows that Mauritius, Cape Verde, and Morocco are among the top three(3) African countries with high preparedness for remote online learning while countries like Niger, Mali, Malawi, Madagascar, and Tanzania lag (Kr\u0026ouml;nke, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This apparent digital divide shows that concerted efforts from all stakeholders ought to be invested to deliberately promote remote learning readiness among African nations with the view to make tertiary education accessible to all.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2: Online Assessment Practices in Ghana during the COVID-19 pandemic\u003c/h2\u003e \u003cp\u003eVarious online assessment forms were employed in Ghana during the pandemic (Sedofia and Ampadu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Baidoo-Anu -Anu et al., 2023). Most of the assessment practices were instructor-specific involving a \"trial and error\" approach with deep concerns over its integrity due to the near absence of proctoring technologies in the country (Baboolal-Frank, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e cited in Baidoo-Anu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most common forms of assessment during the period were online participation grades, group work presentations, randomized multiple-choice questions, random questions during Zoom lectures, \"Hodgepodge\" grading (marks for being present at a Zoom), and feedback to a selected few due to the large class size (Sedofia and Ampadu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Appiah-Adjei 2021, Baidoo -Anu et al, 2023). Others included online discussion via WhatsApp Google Meets, telegram, or learning and content management systems, file uploads, fieldwork, and e-portfolio, each of which when not proctored effectively could lead to academic dishonesty.\u003c/p\u003e \u003cp\u003eGhana, like most African countries, made a significant transition to e-learning due to COVID-19 with milestone investment in LMS, webinars, and training targeted at e-learning adoption in HEI. Sedofia and Ampadu (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) report that e-learning and e-assessment have always existed in the country but COVID-19 \u0026ldquo;made the entire university community take it seriously\u0026rdquo;. However, with major concerns over online assessment integrity due to impersonation and contract cheating, there exist challenges in the adoption of AIRPS in Ghana to improve authorship assurance and academic integrity for remote online assessment. Moreover, the pedagogical potential of technology-mediated learning and assessment is yet to be explored as systematic reviews show that African researchers are woefully lacking in the area of AI in education (Zawacki-Richter et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Artificial Intelligence Remote Proctoring Systems: Benefits and Challenges\u003c/h2\u003e \u003cp\u003eOnline proctoring is the use of digital infrastructure (software or browser extensions) to monitor or supervise the test taker's assessment environment in order to ensure the credibility of an institution's examination processes (Hussein et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With recent advancements in Artificial Intelligence in education, AI for remote proctoring has emerged as a surveillance solution that seeks to increase the legitimacy of online examinations by providing additional security features to combat academic dishonesty- a critical challenge in online examinations (Swauger, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Artificial Intelligence Remote Proctoring System (AIRPS) is an educational assessment innovation designed to ensure academic integrity in an online test environment. Test takers write the exam off-site in adherence to World Health Organisation (WHO) protocols on social gathering and movement during pandemic-like emergencies. The system uses audio-visual and acoustic surveillance technology currently embedded in modern-day laptops with biometric authentication. The proctor records the exam sessions using surveillance technology to monitor students and their test environment via a 360-degree webcam. Suspicious actions are flagged by the software and can later be referred to for further disciplinary actions (Bedford, Gregg, \u0026amp; Clinton, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This module has been used by some prominent universities across Europe to ensure academic integrity, however, the same cannot be said of Ghanaian universities offering distance programs due to context-specific challenges such as technology access.\u003c/p\u003e \u003cp\u003eAccording to Mpahalala et al., (2024), there is an unresolved debate regarding the adoption of online proctoring of examinations in HEI due to issues of credibility, reliability, and validity of the entire examination process. The arguments for AIRPS state that the system affirms academic integrity by providing a form of surveillance technology that curbs cheating behavior(Swauger, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such an understanding may be based on the Panopticon theory of security control on which AIRPS is modeled in which students internalize the idea of being watched (Foucault,1977). Zhakata (2024) calls this the \"Big Brother\" or \"Cyber Spy\" syndrome that may potentially create test-taker anxiety and invade students' privacy. Paredes et al., (2021) and Balash et al., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) report that the intrusive nature of AIRPS can potentially affect a student's achievement score and as such encourage research that balances the benefits over potential risks of AIRPS.\u003c/p\u003e \u003cp\u003eFurther arguments for AIRPS include prompt feedback on multiple choice questions, and providing support for equity, diversity, and inclusivity in the case of persons with disability who are unable to travel long distances to write their exams for fully online programs (Hussein et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Swauger, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is also cost-effective in comparison to the use of human proctors for remote assessment. More importantly, AIRPS allows for educational continuity in the event of a social disruption such as armed conflicts, pandemics, and natural hazards (issues not uncommon in Africa) that make the movement and gathering of students practically impossible.\u003c/p\u003e \u003cp\u003e Notable challenges of AIRPS beside ethical concerns is its potential to foster a culture of discrimination and distrust between students and faculty. For instance, the eye tracking device of an AIRPS is said to be biased towards persons with neurological and visually impairment (Swauger, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, a common test-taking behavior like reading a question aloud or writing it down through can be flagged as a cheating behavior (Ibid). These concerns require investment in AI algorithms that address the challenges identified by stakeholders of the system for e-proctoring.\u003c/p\u003e \u003cp\u003eScholarship in this field indicates that the next 'big thing' after e-learning is an online assessment and the implementation of AI-based proctoring systems has become the 'need of the hour' for distance education and emergency remote assessment (Nigam et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Hwang \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The entire world currently hangs on technology-driven solutions and AI for online assessment cannot be left out of the equation in higher education studies. It is important to mention that the shift toward online assessment and e-proctoring is not merely a response to technological advancement but represents a fundamental transformation in how education is conducted (Terblanche et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2: Theoretical Framework\u003c/h2\u003e \u003cp\u003eThe Unified Theory of Acceptance and Use of Technology(UTAUT) is a widely used framework to identify factors likely to predict factors that can influence the adoption of AI technology in remote online assessment in many contexts. The theoretical model of UTAUT suggests that the actual use of a technology is determined by behavioral intention. Thus the perceived likelihood of adopting an AIRPS is dependent on the direct effect of four key constructs, namely performance expectancy, effort expectancy, social influence, and facilitating conditions. The effect of these predictors is moderated by age, gender, and experience (Venkatesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The constructs from the UTAUT are explained as follows in the context of the current study.\u003c/p\u003e \u003cp\u003ePerformance Expectancy \u003cb\u003e(PE)\u003c/b\u003e is explained as the extent to which both students and lecturers believe that using the AIRPS can help them attain gains in their academic performance. Flexibility in the assessment process and prompt feedback delivery for the students and reduced workload on the part of the lecturers. Research suggests that \u003cb\u003ePE\u003c/b\u003e is the strongest predictor of behavioral intention to use AIRPS because it combines constructs from several competing theories on technology acceptance, notable of which are the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Innovation Diffusion Theory (IDT) and it is significant in both voluntary and mandatory settings (Zhou, Lu \u0026amp; Wang, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Venkatesh, Thong \u0026amp; Xu, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eEffort Expectancy\u003cb\u003e(EE)\u003c/b\u003e is defined as \"the degree of ease associated with the use of the system\" (Venkatesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Thus both students and lecturers are more likely to adopt an AIRPS if they find it to be user-friendly and easy to navigate. Social Influence \u003cb\u003e(SI)\u003c/b\u003e, on the other hand, considers the impact of peers, educators, and institutional norms on students' decisions to use online proctoring systems. Positive endorsements from educators and the influence of peer acceptance play a significant role in shaping students' perceptions and decisions about the adoption of an AIRPS. The effect of \u003cb\u003eSI\u003c/b\u003e is significant when the use of a new technology is mandatory or obligatory due to power dynamics held by the university management and the compliance order (Venkatesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Facilitating Conditions \u003cb\u003e(FI)\u003c/b\u003e refers to the availability of resources and support, such as technical assistance and necessary hardware. This significantly impacts the acceptance of online proctoring systems as students are more likely to adopt these systems when they have access to the required infrastructure and technical support. The moderating variables considered to likely influence any behavioral intention to use an AIRPS in the Ghanaian distance education space are a person's generation, previous online learning experience, gender, and location. It can be noted that \"generation\" and \"location\" are the factors added to Ventakesh's UTAUT model to explain behavioral intention to use an online remote proctoring system. This addition is premised on the fact that there are stereotypical notions in Africa that these factors including gender may determine technology acceptance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe UTAUT theory has been applied in studies across African Higher Institutions (HEI) to determine factors that can influence the adoption of proctored online examinations. In South Africa, Mutambara and Chibisa (2024) sought to identify the characteristics that influence students\u0026rsquo; acceptance of online proctored assessments. Their findings revealed that \u0026ldquo;perceived usefulness\u0026rdquo;, \u0026ldquo;perceived attitude\u0026rdquo;, and \u0026ldquo;question quality\u0026rdquo; all had a direct impact on actual use of online proctoring technologies. On the other hand, \u0026ldquo;perceived social influence\u0026rdquo;, \u0026ldquo;perceived ease of use\u0026rdquo;, and \u0026ldquo;institutional support\u0026rdquo; had an indirect influence. Akindele et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also investigated the key elements influencing the adoption and implementation of online proctored assessments by educational stakeholders in an open university in Nigeria. Their findings suggested that \u0026ldquo;Performance Expectancy\u0026rdquo; (0.27), \u0026ldquo;Social Influence\u0026rdquo; (0.177), and \u0026ldquo;Personal Innovativeness\u0026rdquo; (0.161) had the strongest positive relationship with \u0026ldquo;Behavioral Intention\u0026rdquo; to adopt remote proctored examination.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Methodological Approach","content":"\u003cp\u003e\u003cstrong\u003e3.1: Research design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a concurrent mixed-methods design, integrating both quantitative and qualitative data collection and analysis. This approach allowed for a comprehensive understanding of perceptions toward AI Remote Proctoring Systems (AIRPS) by triangulating numerical trends with contextual insights. The design aligns with Creswell and Creswell\u0026rsquo;s (2018) framework for mixed-methods research, ensuring both breadth and depth in data interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Setting and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Institutional Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected Kwame Nkrumah University of Science and Technology (KNUST) and University of Cape Coast (UCC) based on two criteria: (1) significant distance education enrollment (representing \u0026gt;60% of Ghana\u0026apos;s distance education students; NCTE, 2015), and (2) established online learning infrastructures that could potentially support AIRPS implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 Participant Recruitment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Component:\u003c/strong\u003e Using stratified random sampling, we recruited distance education students stratified by institution and academic level. The target sample size was calculated using G*Power 3.1.9.7 for multiple regression with a medium effect size (0.15), power of 0.80, alpha of 0.05, and seven predictors, yielding a minimum requirement of 103 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQualitative Component:\u003c/strong\u003e We used purposive sampling to select fifteen (15) faculty members with online examination experience. This ensured diversity across demographic characteristics and academic disciplines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Data Collection Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1 Survey Instrument\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe adapted the UTAUT questionnaire (Venkatesh et al., 2003) for the AIRPS context. The instrument included demographic items (8 questions) and UTAUT constructs measured on 5-point Likert scales. The constructs were Performance Expectancy (4 items, \u0026alpha; = 0.87), Effort Expectancy (4 items, \u0026alpha; = 0.82), Social Influence (3 items, \u0026alpha; = 0.79), Facilitating Conditions (4 items, \u0026alpha; = 0.81) and Behavioral Intention (3 items, \u0026alpha; = 0.85)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2 Interview Protocols\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interview protocols were semi-structured, with questions aligned to UTAUT constructs. Sample questions included: \u0026quot;As a faculty member, what benefits do you perceive in using AI-based proctoring for examinations?\u0026quot; and \u0026quot;What challenges would you anticipate if your institution implemented AIRPS?\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Data Collection Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection occurred from March to April 2024. Surveys were distributed via Google Forms on students\u0026rsquo; community platforms and chat groups. Interviews were conducted via Zoom, recorded with consent, and transcribed verbatim.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.1 Quantitative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used Jamovi 2.5.3 for the analysis. After checking assumptions, we employed descriptive statistics for participant characteristics, Mann-Whitney U and Kruskal-Wallis tests for group comparisons (due to non-normal distributions), and multiple linear regression to examine relationships between the UTAUT constructs and behavioral intention to use the AIRPS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.2 Qualitative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted thematic analysis following Braun and Clarke\u0026apos;s (2006) six-phase process, using UTAUT constructs as initial codes while remaining open to emergent themes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Scenario Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo aid understanding of AIRPS, participants were presented with a scenario-based vignette describing a typical remote proctored exam using AI. The vignette included features such as webcam monitoring, biometric authentication, and automated flagging. It was validated by three educational technology experts for clarity and realism. The purpose of the scenario-based approach was to offset the technological deficit and to stimulate dialogue among students and faculty on the potential factors that can influence the adoption of remote e-proctoring systems in the Ghanaian distance education space (Bai et al., 2022). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Reliability and Validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey instrument was piloted with ten postgraduate students for clarity and comprehensiveness. The Cronbach\u0026apos;s alpha for the overall instrument was 0.839, indicating good internal consistency. For qualitative data, trustworthiness was established through member checking, peer debriefing, and maintaining an audit trail.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the KNUST Human and Social Science Research Ethics Committee (HUSSREC) in March 2024. All participants provided informed consent. Data were anonymized, and participation was voluntary with the right to withdraw at any time.\u003c/p\u003e"},{"header":"4.0 Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1: Respondent Characteristics\u003c/h2\u003e \u003cp\u003eA total of 370 valid responses were collected from the study population. Of these, a majority of participants (n\u0026thinsp;=\u0026thinsp;282, 76%) were affiliated with the Institute of Distance Learning at the Kwame Nkrumah University of Science and Technology (IDL-KNUST), while 49 respondents (13.2%) were from the College of Distance Education at the University of Cape Coast (CoDE-UCC) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding educational level, 233 respondents (63%) were enrolled in undergraduate programs, and 135 (36.5%) were pursuing postgraduate studies. Participants\u0026rsquo; fields of study included development studies, industrial finance and investment, engineering, nursing, psychology, human resource management, geography and rural development, commerce, procurement, and supply chain management.\u003c/p\u003e \u003cp\u003eIn terms of gender distribution, 237 respondents (64.1%) identified as male, and 133 (35.9%) as female. Digital literacy was notably high, with 360 respondents (97%) reporting good to excellent proficiency in the use of computers and internet tools for academic purposes. Additionally, 343 participants (92.7%) had prior experience with online assessments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Profile of Respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional Affiliation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDL-KNUST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoDE-UCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePost-graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e361\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e97.8%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGen Z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMillenials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGen X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDigital Literacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOnline Assessment Experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePreferences regarding examination formats were distributed as follows: 169 respondents (45.8%) preferred online examinations administered in a centralized location, 44 (11.9%) favored remotely proctored online examinations, and 156 (42.3%) preferred traditional in-person examinations with human invigilators.\u003c/p\u003e \u003cp\u003eWith respect to generational categorization, 257 respondents (69.5%) identified as Millennials, 77 (20.8%) as members of Generation Z, and 36 (9.7%) as Generation X. While Millennials and Generation Z are typically described as \u0026ldquo;digital natives,\u0026rdquo; Generation X is often referred to as \u0026ldquo;digital immigrants.\u0026rdquo; Nonetheless, all generational cohorts reported familiarity and comfort with the use of digital technologies for academic tasks. In terms of residential location, most respondents (n\u0026thinsp;=\u0026thinsp;258, 69.7%) resided in urban areas, followed by 60 (16.2%) in peri-urban areas, and 52 (14.1%) in rural communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Research Question 1: Influence of Demographic Factors on AIRPS Acceptance\u003c/h2\u003e \u003cp\u003eThe gender dimension in technology acceptance is an important element of the original UTAUT theory and may potentially moderate students acceptance of AIRPS (Ventakesh et al, 2003). A Mann-Whitney U test revealed no statistically significant difference in AIRPS behavioral intention between male (Mdn\u0026thinsp;=\u0026thinsp;3.67, IQR\u0026thinsp;=\u0026thinsp;1.33) and female (Mdn\u0026thinsp;=\u0026thinsp;3.83, IQR\u0026thinsp;=\u0026thinsp;1.17) participants, U\u0026thinsp;=\u0026thinsp;15,048, p\u0026thinsp;=\u0026thinsp;0.467, ε\u0026sup2; = 0.045, 95% CI for effect size [0.001, 0.089](Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed effect size (ε\u0026sup2; = 0.045) falls within Cohen's (1988) small effect range but represents only 4.5% of variance explained. This suggests that gender-based customization of AIRPS implementation strategies may not be necessary. This supports a gender-neutral approach to technology deployment within the study\u0026rsquo;s context. However, the 95% confidence interval for the effect size ranges from near-zero to small-moderate (0.089), indicating some uncertainty about the true magnitude of gender differences.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical Power Considerations\u003c/strong\u003e \u003cp\u003ePost-hoc power analysis indicated 67% power to detect medium effects (d\u0026thinsp;=\u0026thinsp;0.5) with the current sample size, suggesting adequate sensitivity for practically meaningful differences.. This finding is consistent with earlier research by the Institute of Chartered Accountants, Ghana (2023) in which the acceptance rate of AI proctoring solutions for remote online exams was not hinged on gender. This can be explained by the relatively high digital literacy rate among the study sample (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eA Kruskal-Wallis test between generation and behavioral intention (BI) was conducted to determine if a student's generational identity can significantly influence his/her intention to use the e-proctoring system for online assessment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The test revealed no statistically significant difference in behavioral intention to use AIRPS across the different student generations (χ\u0026sup2; = 0.471, p\u0026thinsp;=\u0026thinsp;0.790). The very low effect size (ε\u0026sup2; = 0.00128, 95% CI [0.000, 0.008]) indicates that generational identity accounts for less than 0.13% of the variance in behavioral intention to use AIRPS. According to Cohen's (1988) guidelines, this represents a negligible practical effect. From an implementation perspective, this finding suggests that universities can develop unified AIRPS training and support programs without age-specific modifications. This can potentially reduce implementation costs and complexity. However, the small effect size should be interpreted alongside the study's statistical power (1-β\u0026thinsp;=\u0026thinsp;0.52 for small effects), indicating limited ability to detect small but potentially meaningful differences between generational groups.These findings contradict stereotypical notions about generational adaptations to technology and confirm conclusions that the generational gap in technology acceptance in education seems to be narrowing (Bennet,2023).\u003c/p\u003e \u003cp\u003eSeveral adaptations to the UTAUT theory show that location and experience can be significant predictors of behavioral intentions to use a given educational technology (Venkatesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For instance, Chibisa and Mutambara (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) argue that rural university students in South Africa are in favor of the continued use of proctored examinations likely due to its flexibility and adaptability. However, the p-values of location and previous online examination experience of students in this study were 0.061 and 0.0439 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Their very low effect sizes (ε\u0026sup2; = 0.00953 and 0.00447) show that neither of these factors can determine the adoption of AIRPS within the distance education spaces among the study sample.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender/Generation/Experience/Location and Behavioral Intention to Adopt AIRPS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eε\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender and BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u0026rsquo;s t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eχ\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eε\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneration and BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience and BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation and BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3: Research Question 2: Perception of students and faculty on Performance Expectancy(PE) and Effort Expectancy(EE) of AIRPS for online assessment in Ghana.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Coefficients - BI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eDependent Variable\u0026thinsp;=\u0026thinsp;Behavioral Intention (BI)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003c/p\u003eA multivariate regression analysis results indicate that Performance Expectancy (PE) had the strongest standardized effect (ẞ = 0.3621, p\u0026thinsp;\u0026lt;\u0026thinsp;.001)(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that respondents who believe the AIRPS system is beneficial in completing their examinations are significantly more likely to adopt it. This finding is consistent with Akindele et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) research in Nigeria in which PE (0.27) had the strongest positive relationship with behavioral intention to adopt remote proctored examinations. A thematic analysis of the qualitative interviews suggests that some faculty members prioritize the potential of AIRPS to boost their productivity amidst safety concerns. A professor with over 20 years experience as a supervisor in the conduct of distance examinations noted :\u003cp\u003e\"... the current examination style of moving human proctors across the country to conduct examinations for students is risky and unsustainable. Imagine, in case of an accident\u0026hellip; \"(which God forbid! )\", all these valuable human resources of the nation will be lost. E-assessment is actually what should be done by the distance education units of our universities, where students should be allowed to write from whichever location they prefer\u0026hellip;its all about adaptability to suit our local context\u0026hellip;after all, there are many remote assessments like the GRE that have high integrity. It's all about setting our priorities right\u0026hellip;\"(April, 2024)\u003c/p\u003e \u003cp\u003eThe findings on PE are revealing but inconclusive of faculty's perception of remote proctored examination. For instance, a faculty member with a contradictory viewpoint said:\u003c/p\u003e \u003cp\u003e\"As a lecturer, I will not use online examinations in my course because of what I have seen students do during such an assessment. The students are knowledgeable in the use of computers to cheat and doubt if the e-proctoring systems can be an adequate measure to ensure the integrity of the test\"(Exams officer and invigilator_2024)\u003c/p\u003e \u003cp\u003eEffort Expectancy (EE) also emerged as a significant predictor (ẞ = 0.3088, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) of students adoption intention of AIRPS. The significant and positive relationship between EE and BI supports another core assumption of the UTAUT theory\u0026mdash;that the easier a system is to use, the more likely individuals are to adopt it (Ventakesh et al., 2003). These findings contradict research by Jiang et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in which EE did not significantly and positively affect online proctoring acceptance. They explained that familiarity with the technical requirement of online proctoring technologies may have accounted for the results in their context. However, in Ghana, AIRPS is not a familiar proctoring tool, and as such its perceived ease of use is important to the research participants.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.4: Research Question 3: The role of Social Influence(SI) and Facilitating Conditions(FC) in shaping AIRPS acceptance in Ghana.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSocial Influence (SI) was another significant predictor of AIRPS in the study sample but not the strongest (ẞ = 0.1698, p\u0026thinsp;=\u0026thinsp;0.008). This finding suggests that students' decision to use online proctoring technology is largely dependent on the positive endorsement of AIRPS by university management and regulatory bodies such as the Ghana Tertiary Education Council (GTEC). These results agree with earlier research in Nigeria in which positive peer endorsement was a critical factor in the acceptance of online proctoring technology among distance students (Akindele et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A faculty member highlighted that,\u003c/p\u003e \u003cp\u003e\"...technology is changing the face of assessment and we cannot afford to do things the usual way, if university management says that e-proctoring solution is the new way of online assessment, why not? We (lecturers) will support it\"\u003c/p\u003e \u003cp\u003e(Lecturer and examiner_2024)\u003c/p\u003e \u003cp\u003eInterestingly, Facilitating Conditions (FC) did not significantly predict behavioral intention to use the AIRPS among the study participants (ẞ = 0.0417, p\u0026thinsp;=\u0026thinsp;0.463). This result contradicts Bayaga and Plessis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who stated that FC is a significant predictor of BI to adopt and use Learning Management System(LMS) in developing countries. The results also challenge popular assumptions that students' concern over access to technological resources or support infrastructure may impact their intention to use AIRPS. The qualitative findings revealed that some distance education units in Ghana have invested heavily in infrastructures that encourage online examination for distance learners. One faculty states,\u003c/p\u003e \u003cp\u003e\"online assessment is a \"game-changer\" in how we assess our students and our institution is working towards that by establishing computer labs across all our learning centers...this is because COVID taught us a valuable lesson in education services delivery\u0026hellip;cheating can be resolved by freezing the computers of the test-taker\"\u003c/p\u003e \u003cp\u003e(examiner and board chairman, online assessment committee, 2024 )\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Model Fit\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Measures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c16\" namest=\"c9\"\u003e \u003cp\u003eOverall Model Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe model, based on the constructs of the UTAUT theory to determine behavioral intention to adopt AIRPS in distance education in Ghana demonstrated a multiple correlation coefficient (R) of 0.691. This indicates a moderately strong linear relationship between the predictors and the dependent variable. More importantly, the coefficient of determination (R\u0026sup2;) was 0.478, which means that approximately 47.8% of the variance in behavioral intention can be explained by the four predictors in the model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests a moderate level of explanatory power, indicating that these variables collectively offer substantial insight into what drives users' intentions to engage with the AIRPS system. The F-statistic (F\u0026thinsp;=\u0026thinsp;83.6, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) confirms the model's overall statistical significance, rejecting the null hypothesis that all coefficients are zero. This underscores that the predictors collectively contribute meaningfully to understanding AIRPS adoption in Ghanaian distance education.\u003c/p\u003e \u003cp\u003eThe regression model further tested a wide range of demographic variables (e.g., gender, generation, previous experience, and location) and their interaction effects on to use of AIRPS. Interestingly, as earlier confirmed from the non-parametric T-Tests, none of these demographic variables nor their interactions had statistically significant effects on behavioral intention. For example, differences between gender groups, generational cohorts (Gen Z, Millennials, Gen X), urban versus rural users, and previous experience with similar systems did not predict changes in BI. Interaction terms\u0026mdash;such as gender \u0026times; generation, generation \u0026times; location, or even three-way and four-way interactions\u0026mdash;also failed to reach statistical relevance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5.0 Discussion","content":"\u003cp\u003eThis study investigated perceptions of AI Remote Proctoring Systems among students and faculty in Ghanaian distance education programs through the lens of the UTAUT model. The study provides insight into the factors influencing AIRPS acceptance in this context and challenges some common assumptions about technology adoption in Global South settings where gender, location, generation, and previous online learning experience are assumed to play a key role in technology acceptance in education (Bayaga and Plessis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Demographic Factors and AIRPS Acceptance\u003c/h2\u003e \u003cp\u003eContrary to prevailing assumptions about digital divides in sub-Saharan Africa, the study\u0026rsquo;s findings demonstrate that demographic factors\u0026mdash;gender, generation, location, and prior experience\u0026mdash;did not significantly influence behavioral intention to use AIRPS. This contradicts previous studies that found these factors to be important moderators of technology acceptance (Venkatesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Richardson \u0026amp; Clesham, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The sample suggests that digital literacy may be more widespread across demographic groups than previously assumed, particularly among those enrolled in distance education programs who already navigate digital learning environments.\u003c/p\u003e \u003cp\u003eThe absence of generational differences is particularly noteworthy, as it challenges general stereotypes that older individuals (Gen X) would be less receptive to advanced technologies than their younger counterparts (Gen Z and Millennials). This finding aligns with emerging research suggesting that generational technology gaps may be narrowing in educational contexts (Bennett, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and indicates that implementation strategies need not be heavily differentiated by age group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Key Predictors of AIRPS Acceptance\u003c/h2\u003e \u003cp\u003ePerformance Expectancy emerged as the strongest predictor of behavioral intention to use AIRPS, confirming findings from previous UTAUT studies in educational technology contexts (Akindele et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This suggests that emphasizing the benefits of AIRPS\u0026mdash;such as flexibility, integrity, and efficiency would be crucial for successful implementation in Ghanaian universities. As one faculty participant noted, traditional assessment methods involving human proctors traveling across the country are \"risky and unsustainable,\" highlighting the practical advantages of remote solutions.\u003c/p\u003e \u003cp\u003eThe significant influence of Effort Expectancy indicates that perceived ease of use remains a critical factor for technology adoption, particularly in contexts where advanced technological systems are less common. This aligns with Davis' (1989) original Technology Acceptance Model and suggests that simple, intuitive interfaces would be essential for AIRPS implementation in Ghanaian distance education.\u003c/p\u003e \u003cp\u003eThe significant role of Social Influence reflects the importance of institutional endorsement and peer acceptance in technology adoption decisions. This finding supports Venkatesh et al.'s (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) assertion that social factors are particularly influential in mandatory adoption contexts, as would likely be the case with institutional implementation of AIRPS.\u003c/p\u003e \u003cp\u003eSurprisingly, Facilitating Conditions did not significantly predict behavioral intention to use AIRPS, a finding that seems contrary to studies by Bayaga and Plessis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) on students' acceptance of e-learning systems. This may reflect the high digital literacy of our sample or suggest that participants assumed necessary infrastructure would be provided by their institutions. Alternatively, it may indicate that concerns about infrastructure are overshadowed by perceptions of usefulness and usability.\u003c/p\u003e \u003cp\u003eIn summary, these findings suggest that the influence of PE, EE, and SI on behavioral intention is consistent across different demographic groups, reinforcing the idea that perceptions of usefulness, ease of use, and social encouragement are universal factors in the adoption of AIRPS in case institutions. The lack of significant moderation implies that AIRPS implementation strategies may not need to be heavily tailored by user demographics but should instead focus on improving perceived system utility, ease, and endorsement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Practical Implications for Implementation\u003c/h2\u003e \u003cp\u003eOur findings suggest several practical implications for universities considering AIRPS implementation in Ghanaian distance education programs:\u003c/p\u003e \u003cp\u003eFirst, implementation efforts should prioritize demonstrating system benefits (addressing Performance Expectancy) and ensuring user-friendly interfaces (addressing Effort Expectancy). Training programs should emphasize how AIRPS can enhance assessment flexibility while maintaining integrity. Secondly, institutional leadership should actively endorse and promote AIRPS adoption (leveraging Social Influence), providing clear policies and support for both students and faculty during transition periods. Thirdly, despite the non-significance of Facilitating Conditions in our model, institutions should still ensure adequate technical infrastructure and support, particularly given the known challenges with internet connectivity in some regions of Ghana. Finally, implementation strategies need not be heavily differentiated by demographic factors, allowing for more streamlined, universal approaches to training and support.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.4: Theoretical Implication\u003c/h2\u003e \u003cp\u003eThe findings extend the UTAUT model by demonstrating its applicability in a low-resource, post-pandemic African context. The non-significance of demographic moderators suggest that digital readiness may be more evenly distributed among distance learners than previously assumed. This challenges traditional views of the digital divide and supports calls for more context-sensitive models of technology acceptance in education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Limitations and Future Research\u003c/h2\u003e \u003cp\u003eThis study has several limitations that suggest directions for future research. First, our use of scenario-based vignettes may not fully capture how participants would respond to actual system usage. Future studies should examine perceptions during or after actual implementation experiences. Secondly, our sample was limited to two universities and may not represent all Ghanaian distance education programs. Future research should expand to a wider range of institutions, including private universities and those in more rural regions. Thirdly, generalization of the study's findings should be done with caution as the sample did not meet normality assumptions justifying the use of non-parametric tests. Additionally, the reliance on self-reported data introduces potential social desirability bias. Finally, future research should explore additional factors beyond the UTAUT framework that may influence AIRPS acceptance in African contexts, such as privacy concerns, cultural attitudes toward surveillance, and institutional trust.\u003c/p\u003e \u003c/div\u003e"},{"header":"6.0 Conclusion","content":"\u003cp\u003eThis study contributes to understanding technology acceptance factors for AI Remote Proctoring Systems in Ghanaian distance education, addressing a significant gap in the literature on educational technology implementation in sub-Saharan African contexts. Our findings challenge some prevailing assumptions about demographic influences on technology adoption and confirm the primary importance of perceived usefulness, ease of use, and social influence in predicting acceptance intentions.\u003c/p\u003e \u003cp\u003eFor Ghanaian universities navigating the post-COVID landscape of distance education, our results suggest that successful AIRPS implementation depends primarily on demonstrating system benefits, ensuring user-friendly design, and securing institutional endorsement rather than tailoring approaches to specific demographic groups. As online assessment continues to evolve globally, this research provides valuable insights into how advanced proctoring technologies might be effectively integrated into educational systems with unique infrastructural and cultural contexts.\u003c/p\u003e \u003cp\u003eWhile technological solutions like AIRPS offer promising approaches to assessment integrity challenges, their successful implementation ultimately depends on alignment with local educational needs, values, and resources. Future policy and practice in Ghanaian distance education should therefore approach AIRPS adoption as part of a broader strategy for enhancing educational quality and accessibility while respecting the unique characteristics of the Ghanaian educational landscape.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e: All data generated and analyzed during the study are included in this published article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Not Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e: Our sincere appreciation goes to Stephen Bandoma for his invaluable comments, professional proofreading and referencing support.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdotey, S. K. (2020). What will higher education in Africa look like after COVID-19? World Economic Forum. Accessed 02.02.2025 higher-education-africa-covid19-coronavirusdigital-online/\u003c/li\u003e\n\u003cli\u003eAlessio, H. M., Malay, N., Maurer, K., Bailer, A. J., \u0026amp; Rubin, B. (2017). Examining the effect of proctoring on online test scores. \u003cem\u003eOnline Learning\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 146-161.\u003c/li\u003e\n\u003cli\u003eAkindele, A. T., Adeoye, O. O., Arulogun, O. T., Osunniyi, J. S., Akanbi, R. Y., \u0026amp; Oluwatobi, M. F. (2024). Stakeholder\u0026rsquo;s perception on remote proctored exam in higher educational institutions: insights from lautech open and distance learning centre. \u003cem\u003eMultidisciplinary Journal of Distance Education Studies\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1).\u003c/li\u003e\n\u003cli\u003eAli, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. \u003cem\u003eHigher education studies\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(3), 16-25.\u003c/li\u003e\n\u003cli\u003eAmigud, A., Arnedo-Moreno, J., Daradoumis, T., \u0026amp; Guerrero-Roldan, A. E. (2017). Using learning analytics for preserving academic integrity. \u003cem\u003eInternational Review of Research in Open and Distributed Learning\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(5), 192-210.\u003c/li\u003e\n\u003cli\u003eAppiah-Adjei, G. (2022). COVID-19 and e-assessments in Ghanaian higher education institutions: The case of journalism education. In \u003cem\u003eTeaching and learning with digital technologies in higher education institutions in Africa\u003c/em\u003e (pp. 120-144). Routledge.\u003c/li\u003e\n\u003cli\u003eBaboolal-Frank, R. (2021). Emergency remote learning during the pandemic from a South African perspective. \u003cem\u003eInternational Journal for Educational Integrity\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 22.\u003c/li\u003e\n\u003cli\u003eBai, J. Y., Zawacki-Richter, O., \u0026amp; Muskens, W. (2022, September). Developing strategic scenarios for artificial intelligence applications in higher education. In \u003cem\u003e5th International Open and Distance Learning Conference Proceedings Book\u003c/em\u003e (pp. 47-70).\u003c/li\u003e\n\u003cli\u003eBaidoo-Anu, D., Asamoah, D., Quainoo, E. A., Gyamerah, K., Amoateng, E. Y., \u0026amp; Sasu, E. O. (2023, October). Emergency remote assessment practices in higher education in sub-Saharan Africa during COVID-19. In \u003cem\u003eFrontiers in Education\u003c/em\u003e (Vol. 8, p. 1221115). Frontiers Media SA.\u003c/li\u003e\n\u003cli\u003eBalash, D. G., Kim, D., Shaibekova, D., Fainchtein, R. A., Sherr, M., \u0026amp; Aviv, A. J. (2021). Examining the examiners: Students\u0026apos; privacy and security perceptions of online proctoring services. In \u003cem\u003eSeventeenth symposium on usable privacy and security (SOUPS 2021)\u003c/em\u003e (pp. 633-652).\u003c/li\u003e\n\u003cli\u003eBayaga, A., \u0026amp; du Plessis, A. (2023). Ramifications of the Unified Theory of Acceptance and Use of Technology (UTAUT) among developing countries\u0026rsquo; higher education staffs. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(8), 9689-9714.\u003c/li\u003e\n\u003cli\u003eBedford, W., Gregg, J., \u0026amp; Clinton, S. (2009). Implementing technology to prevent online cheating: A case study at a small southern regional university (SSRU). \u003cem\u003eMERLOT Journal of Online Learning and Teaching\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 230-238.\u003c/li\u003e\n\u003cli\u003eBennett, R. E. (2023). Toward a theory of socioculturally responsive assessment. \u003cem\u003eEducational Assessment\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 83-104.\u003c/li\u003e\n\u003cli\u003eBloemers, W., Oud, A., \u0026amp; Dam, K. V. (2016). Cheating on unproctored internet intelligence tests: Strategies and effects. \u003cem\u003ePersonnel Assessment and Decisions\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 3.\u003c/li\u003e\n\u003cli\u003eBraun, V., \u0026amp; Clarke, V. (2006). Using thematic analysis in psychology. \u003cem\u003eQualitative research in psychology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(2), 77-101.\u003c/li\u003e\n\u003cli\u003eChen, X., Xie, H., Zou, D., \u0026amp; Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. \u003cem\u003eComputers and Education: Artificial Intelligence\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e, 100002.\u003c/li\u003e\n\u003cli\u003eChibisa, a., \u0026amp; Mutambara, D. (2023). Rural university students\u0026rsquo;continuous use of online proctored examinations. \u003cem\u003eJournal of Theoretical and Applied Information Technology\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e(15).\u003c/li\u003e\n\u003cli\u003eCreswell, J. W., \u0026amp; Creswell, J. D. (2017). \u003cem\u003eResearch design: Qualitative, quantitative, and mixed methods approaches\u003c/em\u003e. Sage publications.\u003c/li\u003e\n\u003cli\u003eDavis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. \u003cem\u003eMIS Quarterly\u003c/em\u003e, 319-340.\u003c/li\u003e\n\u003cli\u003eDietrich, N., Kentheswaran, K., Ahmadi, A., Teychen\u0026eacute;, J., Bessi\u0026egrave;re, Y., Alfenore, S., ... \u0026amp; H\u0026eacute;brard, G. (2020). Attempts, successes, and failures of distance learning in the time of COVID-19. \u003cem\u003eJournal of Chemical Education\u003c/em\u003e, \u003cem\u003e97\u003c/em\u003e(9), 2448-2457.\u003c/li\u003e\n\u003cli\u003eFoucault, M., \u0026amp; Deleuze, G. (1977). Intellectuals and power. \u003cem\u003eLanguage, counter-memory, practice: Selected essays and interviews\u003c/em\u003e, \u003cem\u003e205\u003c/em\u003e, 209.\u003c/li\u003e\n\u003cli\u003eGamage, K. A., Silva, E. K. D., \u0026amp; Gunawardhana, N. (2020). Online delivery and assessment during COVID-19: Safeguarding academic integrity. \u003cem\u003eEducation Sciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(11), 301.\u003c/li\u003e\n\u003cli\u003eGhana Tertiary Education Commission (GTEC), 2023. Distance Education Policy Audit: Survey among Ghana\u0026rsquo;s Tertiary Education Institutions. Committee on Partnership for Enhanced and Blended Learning (PEBL), August 2023. [online] Available at: \u0026gt; [Accessed 14.03. 2024].\u003c/li\u003e\n\u003cli\u003eHall, E. A., Spivey, C., Kendrex, H., \u0026amp; Havrda, D. E. (2021). Effects of remote proctoring on composite examination performance among doctor of pharmacy students. \u003cem\u003eAmerican Journal of Pharmaceutical Education\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(8), 8410.\u003c/li\u003e\n\u003cli\u003eH\u0026eacute;bert, C. (2021). Online remote proctoring software in the neoliberal institution: measurement, accountability, and testing culture. \u003cem\u003ein education\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(1), 23-40.\u003c/li\u003e\n\u003cli\u003eHwang, G. J. (2020). E-learning and innovative education: Strategies for adding innovation and value to educational research. \u003cem\u003eInnovating Education in Technology-Supported Environments\u003c/em\u003e, 109-115.\u003c/li\u003e\n\u003cli\u003eHussein, M. J., Yusuf, J., Deb, A. S., Fong, L., \u0026amp; Naidu, S. (2020). An evaluation of online proctoring tools. \u003cem\u003eOpen Praxis\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 509-525.\u003c/li\u003e\n\u003cli\u003eHylton, K., Levy, Y., \u0026amp; Dringus, L. P. (2016). Utilizing webcam-based proctoring to deter misconduct in online exams. \u003cem\u003eComputers \u0026amp; Education\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e, 53-63.\u003c/li\u003e\n\u003cli\u003eJiang, X., Goh, T. T., Chen, X., Liu, M., \u0026amp; Yang, B. (2023). Investigating university students\u0026rsquo; online proctoring acceptance during COVID-19: An extension of the technology acceptance model. \u003cem\u003eAustralasian Journal of Educational Technology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 47-64.\u003c/li\u003e\n\u003cli\u003eJoseph, D., Nethsinghe, R., \u0026amp; Cabedo-Mas, A. (2021). Online teaching and learning during COVID-19: flexible harmonies in Higher Education. In \u003cem\u003eOnline teaching and learning in higher education during COVID-19\u003c/em\u003e (pp. 50-68). Routledge.\u003c/li\u003e\n\u003cli\u003eJurado de Los Santos, P., Moreno-Guerrero, A. J., Mar\u0026iacute;n-Mar\u0026iacute;n, J. A., \u0026amp; Soler Costa, R. (2020). The term equity in education: A literature review with scientific mapping in web of science. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(10), 3526.\u003c/li\u003e\n\u003cli\u003eKarim, M. N., Kaminsky, S. E., \u0026amp; Behrend, T. S. (2014). Cheating, reactions, and performance in remotely proctored testing: An exploratory experimental study. \u003cem\u003eJournal of Business and Psychology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e, 555-572.\u003c/li\u003e\n\u003cli\u003eKitahara, R. T., \u0026amp; Westfall, F. (2007). Promoting academic integrity in online distance learning courses. \u003cem\u003eMERLOT Journal of Online Learning and Teaching\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 265-276.\u003c/li\u003e\n\u003cli\u003eKr\u0026ouml;nke, M. (2020). \u003cem\u003eAfrica\u0026apos;s digital divide and the promise of e-learning\u003c/em\u003e. Accra: Afrobarometer.\u003c/li\u003e\n\u003cli\u003eMaboe, K. A., \u0026amp; Tomas, N. (2023). Online assessments and COVID-19: A qualitative study of undergraduate nursing students in Southern Africa. \u003cem\u003eInternational Journal of Africa Nursing Sciences\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 100590.\u003c/li\u003e\n\u003cli\u003eMaphalala, M. C., Mpofu, N., \u0026amp; Adigun, O. T. (2024). The dynamics of e-assessment in South African Higher Education: Narratives for and against proctoring in higher education during COVID-19 and beyond. In \u003cem\u003eOnline teaching and learning in higher education: Issues and challenges in an African context\u003c/em\u003e (pp. 183-199). Cham: Springer Nature Switzerland.\u003c/li\u003e\n\u003cli\u003eMarinoni, G., Van\u0026rsquo;t Land, H., \u0026amp; Jensen, T. (2020). The impact of Covid-19 on higher education around the world. \u003cem\u003eIAU global survey report\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 1-17.\u003c/li\u003e\n\u003cli\u003eNational Council for Tertiary Education (2015). Statistical Digest 2014/2015. Accra: NCTE.\u003c/li\u003e\n\u003cli\u003eNigam, A., Pasricha, R., Singh, T., \u0026amp; Churi, P. (2021). A systematic review on AI-based proctoring systems: Past, present and future. \u003cem\u003eEducation and Information Technologies\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(5), 6421-6445.\u003c/li\u003e\n\u003cli\u003ePokhrel, S., \u0026amp; Chhetri, R. (2021). A literature review on the impact of the COVID-19 pandemic on teaching and learning. \u003cem\u003eHigher education for the future\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 133-141.\u003c/li\u003e\n\u003cli\u003eRichardson, M., \u0026amp; Clesham, R. (2021). Rise of the machines? The evolving role of Artificial Intelligence (AI) technologies in high-stakes assessment. \u003cem\u003eLondon Review of Education\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 1-13.\u003c/li\u003e\n\u003cli\u003eSedofia, J., \u0026amp; Ampadu, E. (2022). Assessment in higher education in Ghana amid the COVID-19 pandemic: Charting the challenges and paradoxes. In \u003cem\u003eTeaching and Learning with Digital Technologies in Higher Education Institutions in Africa\u003c/em\u003e (pp. 90-105). \u003c/li\u003e\n\u003cli\u003eSwauger, S. (2020). Our bodies encoded: Algorithmic test proctoring in higher education. \u003cem\u003eCritical digital pedagogy\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eTerblanche, W., Lubbe, I., Papageorgiou, E., \u0026amp; van der Merwe, N. (2023). Acceptance of e-learning applications by accounting students in an online learning environment at residential universities. \u003cem\u003eSouth African Journal of Accounting Research\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 35-61.\u003c/li\u003e\n\u003cli\u003eVenkatesh, V., Morris, M. G., Davis, G. B., \u0026amp; Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. \u003cem\u003eMIS Quarterly\u003c/em\u003e, 425-478.\u003c/li\u003e\n\u003cli\u003eVenkatesh, V., Thong, J. Y., \u0026amp; Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. \u003cem\u003eJournal of the Association for Information Systems\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(5), 328-376.\u003c/li\u003e\n\u003cli\u003eWuthisatian, R. (2020). Student exam performance in different proctored environments: Evidence from an online economics course. \u003cem\u003eInternational Review of Economics Education\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e, 100196.\u003c/li\u003e\n\u003cli\u003eZawacki-Richter, O., Mar\u0026iacute;n, V. I., Bond, M., \u0026amp; Gouverneur, F. (2019). A systematic review of research on artificial intelligence applications in higher education\u0026ndash;where are the educators? \u003cem\u003eInternational journal of educational technology in higher education\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 1-27.\u003c/li\u003e\n\u003cli\u003eZhakata, N. (2023). Retooling online proctoring technology in the Fourth Industrial Revolution learning contexts\u0026mdash;from Big Brother to Learning Buddy. In \u003cem\u003eAcademic Quality and Integrity in the New Higher Education Digital Environment\u003c/em\u003e (pp. 117-130). Chandos Publishing.\u003c/li\u003e\n\u003cli\u003eZhou, T., Lu, Y., \u0026amp; Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. \u003cem\u003eComputers in human behavior\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4), 760-767.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kwame Nkrumah University of Science and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI proctoring, UTAUT, distance education, online assessment, educational technology, technology acceptance, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-6924498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6924498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic accelerated the adoption of online education across Africa, necessitating innovative approaches to assessment integrity. This study explores Ghanaian students' and faculty perceptions of AI Remote Proctoring Systems (AIRPS) in distance education using the Unified Theory of Acceptance and Use of Technology (UTAUT). A concurrent mixed-methods design (n=370) revealed that performance expectancy (β=0.37, p\u0026lt;.001) and effort expectancy (β=0.31, p\u0026lt;.001) were the strongest predictors of adoption intention, followed by social influence (β = 0.17, p = .008). However, facilitating conditions were not significant. Contrary to expectations, demographic variables did not moderate these relationships. Findings suggest the need for training protocols that emphasize AIRPS benefits over perceived barriers in the Ghanaian distance education space.\u003c/p\u003e","manuscriptTitle":"Factors Influencing AI Remote Proctoring Acceptance in Ghanaian Distance Education: A Mixed-Methods UTAUT Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-19 08:04:54","doi":"10.21203/rs.3.rs-6924498/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4535d766-4b0b-446c-9ac6-d45b6de8219a","owner":[],"postedDate":"June 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-19T08:04:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-19 08:04:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6924498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6924498","identity":"rs-6924498","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}