AI in Universities: The Good, the Bot, and the Ugly Truths

AI in Universities: The Good, the Bot, and the Ugly Truths | 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 AI in Universities: The Good, the Bot, and the Ugly Truths MICHAEL ADELANI ADEWUSI, CHRISTINE AINEBYOONA, OLA TOKUNBO ODEKEYE, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6883202/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 Artificial intelligence (AI) is reshaping higher education, offering opportunities for personalized learning, enhanced efficiency, and improved campus management. However, its adoption also presents challenges such as ethical concerns, disparities in access, and over-reliance on technology. This study explores the benefits and challenges of AI integration in two Ugandan universities, focusing on its impact on teaching, learning, and institutional administration. Using a mixed-methods approach, data were collected through surveys from both students (123) and faculty members (43) across some Ugandan universities, complemented by interviews. Quantitative data were analyzed using ANOVA to assess differences in perceptions of AI adoption, while qualitative data provided deeper insights into concerns surrounding data privacy, infrastructure limitations, and faculty readiness. The findings revealed significant disparities in AI adoption, with institutions possessing better resources and access to AI tools reporting more positive perceptions of its effectiveness. The study also highlighted concerns over unequal access to AI-driven educational tools, emphasizing the need for targeted policy interventions. Tabular presentations illustrated variations in AI adoption levels, showcasing both the potential and challenges faced by different institutions. Qualitative insights underscored fears of reliance on AI at the expense of human interaction, as well as the necessity for data protection measures. The study concluded that while AI adoption in Ugandan universities is still at a nascent stage, there is a strong shared recognition among students, faculty, and administrators of its potential to enhance teaching, learning, and administrative efficiency, yet significant barriers such as limited infrastructure, unequal access to AI tools, and insufficient training hinder its widespread implementation. The study recommends the strategic investments in digital infrastructure to ensure equitable AI access, the development of policies that prioritize inclusivity, and regular training programs for faculty and administrators to enhance AI literacy. Universities must balance AI adoption with ethical considerations, ensuring that technological advancements do not exacerbate educational inequalities. AI Higher Education Adaptive Learning AI Adoption Educational Technology AI Ethics Introduction Artificial intelligence (AI) is rapidly reshaping university education, offering new ways to enhance teaching, learning, and administration (Zawacki-Richter et al 2024). It refers to the simulation of human intelligence by machines, particularly systems capable of learning, reasoning, and problem-solving (Clegg & Sarker, 2024). In educational contexts, AI encompasses tools such as intelligent tutoring systems, adaptive learning platforms, predictive analytics, and automated administrative technologies that enhance decision-making and personalize the learning experience. From adaptive learning platforms and predictive analytics to automated grading and personalized student support, AI holds the potential to create more responsive and efficient educational environments (Singh, 2023; Sajja et al., 2024). These advancements enable universities to analyze vast amounts of data in real time, allowing educators to tailor instruction to individual student needs and streamline administrative operations. However, the integration of AI into education is not without challenges, especially in developing countries where infrastructure, funding, and technical expertise remain limited (Singh, 2023). Much of the research and development in AI-powered education has concentrated on institutions in developed nations, where strong digital infrastructure and consistent funding facilitate the smooth deployment of AI tools (Yigitcanlar et al., 2024). In contrast, universities in sub-Saharan Africa, including Uganda, grapple with issues such as underfunding, unreliable internet connectivity, and a shortage of trained personnel (Atuahene & XuSheng, 2024; Bulathwela et al., 2024). These challenges not only slow AI adoption but also deepen existing inequalities in access to quality education. Two major areas where AI is transforming education globally are personalized learning and automated university administration. For instance, platforms like ALEKS and Knewton are helping students improve their performance by adapting content to each learner’s progress and style (Ahmad et al., 2025; Ayeni et al., 2024). Meanwhile, universities are also deploying AI-powered administrative systems, including chatbots, predictive analytics tools, and automated enrollment platforms, to streamline admissions, offer real-time student support, and reduce workload on staff (George & Wooden, 2023). While these innovations show great promise, they also raise ethical concerns related to data privacy, algorithmic fairness, and the reduction of human interaction in student services (Rane et al., 2024). However, in Uganda, the integration of AI into university education remains highly uneven due to persistent structural and institutional challenges (Mukasa, 2024). One of the most pressing issues is unreliable internet connectivity, which continues to affect many universities, particularly public institutions and regional campuses. Internet speeds are often too slow to support AI-powered platforms, and outages are common during peak learning hours (Mukasa, 2024). Faculty training presents another significant obstacle. While lecturers express growing interest in leveraging AI to support student learning and streamline grading or feedback processes, most have not received specialized training or professional development in digital pedagogy or AI use (Nalubega & Uwizeyimana, 2024). These gaps are compounded by limited budgets, bureaucratic procurement systems, and the absence of clear national or institutional strategies guiding AI integration (Mukasa, 2024; Nalubega & Uwizeyimana, 2024). To better understand these realities, this study examined two universities representing different institutional contexts, one public and one private. The public institution had begun integrating AI in selected administrative processes and limited teaching tools but faced obstacles such as over-enrollment and insufficient IT infrastructure. The private institution, on the other hand, showed greater readiness and engagement with AI applications, supported by more flexible funding and manageable student populations. This contrast provided a valuable lens through which to explore the broader challenges and opportunities of AI adoption in Uganda’s higher education landscape. To guide this study, it draws upon two key theoretical models: the Technology Acceptance Model (TAM) and the Digital Divide Theory. TAM helps explain how perceived usefulness and ease of use influence the acceptance and integration of AI technologies among faculty and students (Pillai et al., 2024). Meanwhile, the Digital Divide Theory underscores how structural inequalities, such as infrastructure, funding, and digital literacy, can affect the equitable distribution and usage of AI in education (Memon & Memon, 2025). Together, these theories offer a foundation for analyzing the patterns, perceptions, and institutional readiness observed in the Ugandan higher education sector. Research Purpose and Objectives The purpose of this study is to examine how artificial intelligence (AI) technologies are being adopted and perceived in university education in Uganda, with a focus on their role in teaching, learning, and administrative functions. It also aims to identify barriers and propose actionable strategies for inclusive, ethical, and sustainable AI integration in higher education settings. To achieve this, the study is guided by the following specific objectives: To assess how AI technologies are currently being used in teaching, learning, and administrative operations in selected Ugandan universities. To evaluate the impact of AI-powered administrative tools on institutional efficiency and the delivery of student support services in these universities. To identify context-specific policy measures and strategic interventions that can promote equitable access, infrastructure development, and ethical use of AI in Uganda’s higher education system. Research Questions To achieve its objectives, the study is guided by the following research questions: How are AI technologies currently being used in teaching, learning, and administrative functions in Ugandan universities? In what ways have AI-powered administrative tools improved institutional efficiency and student support services? What specific policy measures and strategic interventions can support equitable and sustainable AI adoption in Ugandan universities? Hypotheses H 1 : There is no statistically significant difference in AI adoption levels between public and private universities in Uganda. H 2 : There is no statistically significant difference in perceived institutional efficiency between universities using AI-powered administrative tools and those that do not. Literature Review A study by Khoalenyane and Ajani ( 2024 ), systematically reviewed AI-enhanced adaptive learning platforms and reported their potential to personalize learning. However, these tools were often underutilized due to infrastructural gaps and limited faculty expertise, issues that mirror Uganda’s own digital challenges. In the same vein, Maphalala and Ajani ( 2025 ) demonstrated how institutions implemented intelligent tutoring systems, plagiarism detectors, and content recommendation engines. These AI tools, when effectively integrated, improved student engagement, academic performance, and satisfaction by promoting a personalized and adaptive learning experience. These findings highlight the transformative potential of AI in enhancing educational delivery, while also emphasizing the practical limitations posed by insufficient digital infrastructure and capacity. The studies of Patel and Ragolane ( 2024 ), who investigated AI deployment in student registration systems, AI-driven helpdesks, and course planning tools. Their research revealed that these technologies significantly improved institutional responsiveness and efficiency by automating routine tasks and facilitating real-time support. Nevertheless, they also identified systemic barriers including socio-economic disparities, digital divides, and data-related ethical concerns. In another study, Goosen and Mugumo ( 2024 ) proposed an AI integration framework for academic advising, admissions, and performance tracking. Their results confirmed that efficiency gains were most pronounced when ethical policies were in place, highlighting the need for governance mechanisms to accompany technological implementation. Chakraborty ( 2024 ) cautioned against unchecked deployment of AI tools, documenting ethical dilemmas like algorithmic bias, lack of transparency, and student consent violations in adaptive learning systems. Further, Ajani, Gamede, and Matiyenga ( 2024 ) found low awareness of AI risks among faculty and students, reinforcing the urgency of targeted AI ethics training and rights-based digital literacy campaigns. To bridge these ethical and infrastructural gaps, Funda and Mbangeleli ( 2024 ), along with Airaj ( 2024 ), proposed policy reforms centered on expanding broadband infrastructure, strengthening faculty capacity in AI, and promoting collaboration between institutions. They argue that AI governance in African universities must be grounded in culturally relevant, inclusive policies to avoid reinforcing existing educational inequities. These insights collectively support this study’s aim to develop context-specific, ethical, and sustainable AI strategies for Uganda’s higher education sector. Methodology This study adopted a mixed-methods approach. As a pragmatic researcher, I believe that neither quantitative nor qualitative methods alone can adequately capture the multifaceted realities of AI integration in higher education, particularly in a context like Uganda’s, where institutional capacities and access to digital tools vary widely. Therefore, combining these methods was essential to obtaining a comprehensive understanding of the current state, perceived value, and potential equity-related implications of AI technologies. The research design was both explanatory and comparative, structured to explore the extent of AI use, analyze its impact on university administration and student services, and propose relevant interventions. This design made it possible to capture both broad statistical patterns and context-specific insights across two carefully selected universities, a public and a private institution. These were not named for ethical reasons but were chosen based on their differing levels of digital maturity, infrastructure, and resource availability, making them ideal for a comparative analysis of AI adoption in diverse settings. The target population included students, faculty members, and administrators actively involved in teaching, learning, or administrative functions. These groups were selected due to their direct interaction with AI systems, whether through classroom instruction, digital service delivery, or institutional policy development. A stratified sampling technique was used for selecting students and faculty to ensure representation across academic faculties and departments, while purposive sampling identified administrators and IT personnel with direct exposure to AI tools. Table 1: Summary of the study population and sampling Participant Category Population Estimate Sample Size Sampling Method Students ~5000 123 Stratified Random Sampling Faculty Members ~300 28 Stratified Random Sampling Administrators ~50 15 Purposive Sampling Total — 166 — Data Collection Instruments were designed to directly reflect the three study objectives: A structured questionnaire was administered to students and faculty. It included closed-ended and Likert-scale items capturing awareness, usage patterns, perceived effectiveness, and ethical concerns regarding AI. Semi-structured interviews were conducted with administrative and IT staff to gain insights into institutional practices, resource barriers, and policy perspectives. These allowed for deeper exploration of qualitative themes. To ensure data quality, the questionnaire was reviewed by experts to establish content and face validity. Reliability was assessed using Cronbach’s alpha for internal consistency. For the qualitative component, trustworthiness was enhanced through triangulation, member checking, and use of NVivo for systematic coding and thematic analysis. In terms of data management, quantitative data was entered, cleaned, and analyzed using SPSS Version 25. Descriptive statistics (means, standard deviation, frequencies) described AI usage trends, while ANOVA tested for differences in perceptions between public and private university contexts. The qualitative interview transcripts were analyzed using thematic analysis, generating rich categories on AI implementation, challenges, and policy gaps. Finally, ethical standards were rigorously maintained. Participants provided informed consent, and their responses were anonymized to ensure confidentiality. Results Thematic Analysis of Coded Responses Research Question 1 : How are Ugandan universities currently using AI in teaching, learning, and administration? Table 1: AI in teaching, learning, and administration Theme Faculty staff Students Administrators 1. Perceptions of AI Integration in Teaching and Learning Many faculty members feel that AI tools are still a long way from becoming part of everyday teaching. As one lecturer explained, while tools like Grammarly and Turnitin are helpful, they are mostly used for writing support, not actual instruction. There's a sense that more training is needed before AI can be used more meaningfully in classrooms. Students notice that some lecturers are trying to integrate AI-based platforms like Google Classroom and Moodle, which offer a bit of personalization. But they also feel this is not consistent across courses or departments. Some students benefit from it, while others do not even get to see those tools in use. From the administrative side, there is acknowledgment that AI has not yet taken hold in teaching. Despite investments in digital learning platforms, administrators say many lecturers still rely on traditional methods, partly due to a lack of comfort or familiarity with AI tools. 2. Use of AI in Administrative and Support Functions Faculty generally see administrative AI as a relief. One lecturer pointed out that the chatbot used by the university has taken a load off their shoulders by answering student questions, which allows them to focus more on teaching and mentoring. Students appreciate how AI helps them get faster responses from the university. Whether it is about their schedules, admissions, or campus queries, they find chatbots convenient and efficient. For many, it reduces the long queues and delays they used to experience. Administrators are proud of how AI is improving service delivery. They shared that chatbots and AI dashboards have helped streamline tasks like admissions, inquiries, and records management. It makes their work more efficient and supports smoother campus operations. 3. Challenges Hindering Effective AI Adoption Faculty express frustration with infrastructure limitations. Many want to use AI tools but are held back by unstable internet, lack of computers, and limited exposure to how these tools work. One lecturer said ‘ they’d be more open to AI if proper training and tech support were available’ . Students feel the same pinch. Some have access to digital learning platforms, while others do not. A student explained that it often depends on the lecturer and the department, ‘ some are equipped, others are not’ . This creates a feeling of inequality among students. Administrators say the challenges go deeper. They talk about budget limitations, power outages, and old systems that are hard to upgrade. Even when AI tools are introduced, sustaining them is a real challenge without reliable infrastructure and consistent funding. 4. Shared Vision and Opportunities for AI in Higher Education Despite the hurdles, faculty are hopeful. One lecturer shared that ‘ they’d love to use AI more, especially to track student progress and provide tailored support, if they could get the right kind of training and tools’ . Students are equally enthusiastic. They believe AI could help them learn better, especially when it adapts to their pace and gives them quicker, clearer feedback. Many see it as a step toward a more student-centered learning experience. Administrators also have a positive outlook. They see AI as a powerful tool for improving how the university operates, from data analysis to student retention. They believe that with the right investments and policies, AI can transform the entire educational experience. Table 2: Description statistics on how AI technologies are currently being used Survey Item Mean (M) Standard Deviation (SD) % of Agreement I use AI tools in teaching and assessment (e.g., Turnitin, Moodle AI tools). 3.45 1.12 61% AI is integrated into our learning platforms for personalised instruction. 3.22 1.18 54% Our university uses chatbots or AI-driven systems for admin tasks. 3.90 0.95 77% Infrastructure limitations hinder AI adoption in teaching and learning. 4.30 0.84 88% Faculty are adequately trained to implement AI in academic delivery. 2.60 1.02 39% Interpretation: While the current use in teaching and learning remains limited, primarily involving basic tools like Turnitin and Grammarly, there is a noticeable shift toward more impactful applications in administrative functions, particularly through chatbots and digital dashboards that have improved communication and efficiency. Students and faculty highlight the inconsistent use of learning platforms such as Moodle and Google Classroom, and only 39% of faculty feel adequately trained to implement AI effectively. Infrastructure remains a major concern, with 88% of respondents citing unreliable internet and outdated equipment as key barriers. However, the overall sentiment across faculty, students, and administrators is one of cautious optimism. With increased investment in digital infrastructure and targeted training programs, stakeholders believe that AI holds transformative potential to make education in Uganda more personalized, efficient, and inclusive. Research Question 2 : How do institutional resources and access to AI tools influence perceptions of its effectiveness? Table 3: Institutional resources and AI tools perceptions Theme Faculty staff Students Administrators Resource Availability Shapes AI Perceptions Faculty staff in the public institutions often feel left behind due to limited access to AI tools. One faculty member noted, “In private universities, there’s visible investment in technology. Here, we’re still struggling with access to functional computers, so AI seems out of reach.” Students, too, notice the contrast between institutions. Many shared that while peers in better-resourced universities use AI daily, they are stuck in outdated labs or manually managed learning environments. This inequality influences how they perceive AI’s usefulness. Administrators acknowledge that resource disparities are a major roadblock. They admit that without the proper infrastructure like the reliable internet, modern systems, and enough devices, AI cannot be effectively adopted, especially in public universities operating under tight budgets. Digital Divide Between Universities Faculty in underfunded universities feel that the digital divide is growing. One lecturer shared, “We see what’s possible elsewhere, but we’re barely getting by with old projectors and slow internet. AI feels like a luxury.” Students highlighted how the divide is real and frustrating. As one put it, “We hear about AI tools, but in our labs, half the computers don’t even turn on. It’s hard to be excited about tech we can’t use.” Administrators explained that institutional type directly affects AI readiness. They pointed out that private institutions often have flexible funding structures, while public ones face bureaucratic hurdles, making it harder to keep up with AI trends. Lack of Training Affects Perceived AI Effectiveness Faculty agree that even when tools are available, they are not always confident using them. “AI sounds great in theory, but we’ve never been trained. Without proper exposure, it’s hard to see how it fits into our teaching,” Students notice that some of their lecturers shy away from digital tools. One student commented, “Sometimes, the tools are there, but the lecturer doesn’t use them properly. I think it’s because they haven’t been trained.” Administrators admitted that training has not kept pace with AI adoption. They noted that many of their staff are willing to learn but need structured workshops and consistent support to apply AI tools effectively. AI Perceived as a Tool for Efficiency Faculty who has managed to experiment with AI tools find them helpful. One said, “Automated grading saves me hours every week, but unfortunately, not everyone in the department has access to these tools.” Students appreciate how AI helps streamline processes. “I get quicker feedback on my assignments now, and accessing course materials is easier,” Administrators are clear about AI’s operational benefits. “AI chatbots and data dashboards have made it easier to handle student inquiries and analyze trends. But cost is a barrier. Only a few departments have these tools,” an administrator explained. Table 4: Description statistics on institutional resources and access to AI tools influence perceptions of its effectiveness Survey Item Mean (M) Standard Deviation (SD) % Agreement AI has reduced the administrative workload for staff. 3.88 0.88 75% Students receive timely support through AI chatbots or digital assistants. 3.72 0.92 71% Our admissions and registration systems benefit from AI tools. 3.95 0.80 79% AI tools in administration have increased transparency and efficiency. 3.70 0.94 70% The use of AI for institutional management is still limited to pilot programs. 4.10 0.76 83% Interpretation: Across faculty, students, and administrators, there is a shared concern that inadequate infrastructure, especially in public institutions, is holding back meaningful AI adoption. Faculty feel frustrated by the lack of access to functioning computers and stable internet, while students point to outdated labs and limited exposure to digital tools. Administrators confirm that budget constraints and slow decision-making processes continue to widen the digital divide. These limitations have created a perception of AI as more of a future aspiration than a current solution. Even when AI tools are introduced, many faculty feel underprepared, and students observe that lecturers often lack the training to use them effectively. Administrators agree that professional development hasn't kept pace with the rollout of AI tools. Still, where AI has been implemented, such as in automated grading and chatbots, there is strong agreement that these tools improve efficiency. Survey data supports this, with 75% of respondents acknowledging reduced administrative workload and 79% recognizing improved admissions systems. However, the fact that 83% still see AI tools as limited to pilot phases underscores a gap between recognition of AI’s benefits and its full-scale implementation. To bridge this gap, the study points to a clear need for targeted investments in both infrastructure and staff training. Research Question 3: What policy measures can support AI-driven innovation while ensuring equitable access? Table 5: Policy measures to support AI Theme Faculty staff Students Administrators Need for Government Funding & Infrastructure Development Faculty members emphasize that without significant infrastructure upgrades, AI cannot be effectively integrated into teaching. One faculty respondent shared, “We have the desire, but not the tools. Until the government invests in ICT infrastructure, we’ll remain limited to theory.” Students echo the call for government support, pointing out that their learning environment is constrained by outdated or inaccessible technology. One student said, “We can’t talk about AI if the computers barely work. We need help from higher up.” Administrators are vocal about this need, too. They believe the government should prioritize ICT development across institutions, not just a select few. “Without public investment in digital infrastructure,” one stated, “AI will remain a buzzword, not a practice.” Mandatory AI Training for Faculty Faculty see training as a critical enabler. As one lecturer put it, “We’re not resistant to AI, we’re just not trained. If workshops were part of our professional development, we’d adopt AI more confidently.” Students have noticed that some lecturers are hesitant or inconsistent in using AI tools. “It’s not that they don’t care,” one student explained, “They just haven’t been shown how.” Administrators recognise that AI training must be institutionalised. They agree that faculty development programmes must evolve to include AI competencies. “It shouldn’t be optional anymore,” one administrator emphasised. Equitable AI Resource Allocation Faculty in public institutions stress that AI integration should not be a privilege of private universities. “Our students deserve the same access,” one said. “The government and university management must address this gap.” Students from underfunded universities feel left behind. One said, “It’s not fair that others get personalized AI tutors while we struggle with old equipment. We need equal access if we’re all expected to compete globally.” Administrators call for policy-driven, equitable distribution of digital resources. “Public universities need prioritised support,” one explained, “so students from less privileged backgrounds can benefit just as much as those in elite institutions.” Policy Guidelines for Ethical AI Use Faculty are deeply concerned about the ethics of AI in education. One noted, “AI must not become a tool for unfair grading or profiling. Clear rules and oversight are essential to protect students.” Students worry about how their data is used. “I want AI to help me, not judge me,” one student said. “There should be rules so it doesn’t become invasive or biased.” Administrators agree that strong ethical policies are needed. “We need institutional frameworks that safeguard privacy, ensure fairness, and build trust among users,” one administrator stated. Table 6: Description statistics on specific policy measures and strategic interventions Survey Item Mean (M) Standard Deviation (SD) % on Agreement The government should invest more in digital infrastructure for universities. 4.50 0.68 91% AI training should be mandatory for academic staff. 4.35 0.72 88% Universities need clear policies on data privacy and AI ethics. 4.40 0.69 89% Public-private partnerships can help scale AI implementation. 4.22 0.80 85% Equity in AI access between public and private universities is a key priority. 4.48 0.66 90% Interpretation: Findings reveal a shared urgency among faculty, students, and administrators for robust policy measures to support AI adoption in Ugandan universities. Participants widely agree that without substantial government investment in digital infrastructure, the integration of AI will remain limited to a privileged few institution. Faculty express frustration at the gap between their willingness to use AI and the lack of available tools; students point to outdated equipment and under-resourced labs; while administrators emphasize that broad, inclusive government funding is essential for meaningful, system-wide progress. This collective sentiment is reinforced by the data, with 91% of respondents calling for infrastructure investment and 90% stressing the importance of equitable access. Additionally, the findings highlight that lack of training, not lack of interest, is a key barrier. Faculty, students, and administrators alike support making AI training mandatory, which aligns with the 88% of respondents who endorsed this idea. Ethical concerns also feature prominently: 89% of participants agree on the need for clear policies to safeguard data privacy and prevent algorithmic bias. Moreover, the support for public-private partnerships (85%) suggests that sustainable AI adoption will require a collaborative effort combining policy, training, and cross-sector investment to ensure all universities, regardless of resources, can participate in and benefit from AI advancements. Research hypotheses Research hypothesis 1: There is no statistically significant difference in AI adoption levels between public and private universities in Uganda. Table 7: ANOVA analysis on AI adoption levels between public and private universities in Uganda Source Sum of Squares df Mean Square F Sig. Between Groups 1.333 1 1.333 0.733 0.393 Within Groups 300.800 164 1.835 Total 302.133 165 Interpretation: There is no statistically significant difference in AI adoption levels between public and private universities in Uganda [F (1, 164) = 0.733; p > 0.05]. This result indicated that both students and the lecturers from the universities (public and private) perceived the AI adoption similarly with no differences among them. This further implies that any efforts to promote the adoption of AI can be directed uniformly across the universities, as students and lecturers share similar views about AI's potential benefits. Research hypothesis 2: There is no statistically significant difference in perceived institutional efficiency between universities using AI-powered administrative tools and those that do not. Table 8: ANOVA analysis on faculty and students in universities that use AI-powered administrative tools Source Sum of Squares df Mean Square F Sig. Between Groups 18.625 1 18.625 6.608 0.011 Within Groups 461.541 164 2.815 Total 480.167 165 Interpretation: There is a statistically significant difference between faculty and students in universities that use AI-powered administrative tools that report higher satisfaction with institutional processes than those in universities without such tools (p < 0.05). This indicates disparities in access across institutions. This result implies that there is or are disparities in how public and private universities provide or utilize AI tools which could be due to differences in funding, infrastructure, or institutional priorities. The limited access to AI tools in the public institutions, may negatively affect how students and faculty perceive AI’s effectiveness. The result shows the importance of equitable access to AI tools as a key determinant of how effectively institutions can integrate AI into education. Discussion The findings from this study reveal that while AI adoption in Ugandan universities is gradually increasing, it is still largely confined to administrative functions and plagiarism detection tools, rather than core teaching and learning activities. Many faculty members and students reported that tools like Grammarly and Turnitin were commonly used to assist in academic writing and plagiarism detection. However, fully adaptive AI-powered learning systems remain largely underutilized, with only a few lecturers using platforms like Google Classroom and Moodle to personalize assignments. These findings align with research by McGrath et al. ( 2023 ), who noted that AI has tremendous potential to personalize learning, enhance administrative efficiency, and improve student engagement, yet its full implementation in educational institutions remains a challenge. One of the major obstacles to AI integration in Ugandan universities is the lack of adequate digital infrastructure and technical training for faculty members. Respondents noted that many lecturers had limited exposure to AI technologies, leading to reluctance or uncertainty in adopting AI-based teaching methods. Furthermore, public institutions, in particular, face challenges such as outdated computer labs, unstable internet access, and inadequate technical support, making it difficult to implement AI tools effectively. Heaton et al. ( 2023 ) found similar challenges in other developing regions, where private universities, with more financial flexibility, tend to adopt cutting-edge educational technologies more readily than public institutions. If Ugandan universities hope to fully embrace AI-driven learning, it is essential to invest in infrastructure, offer continuous faculty training, and develop institutional policies that support AI integration (Gikunda, 2023 ). This study also underscores the critical role of institutional resources in shaping perceptions of AI effectiveness. The findings suggest that respondents from private universities have a more favorable view of AI, largely because they have better access to AI tools and infrastructure. In contrast, respondents from public universities expressed concerns that the limited availability of AI technologies makes them less relevant in their academic environment. This aligns with research by Heaton et al. ( 2023 ), which highlights that institutions with stronger financial backing tend to integrate AI more effectively than those with limited funding. However, despite differences in access to AI tools, this study found no significant difference (p > 0.05) in how students and faculty perceive AI’s potential benefits across public and private universities. This suggests that the value of AI is widely recognized, regardless of institutional type. McGrath et al. ( 2023 ) similarly found that faculty and students across different educational institutions acknowledged AI’s transformative potential, even in cases where access was limited. This is an important finding because it means that policymakers can implement AI adoption strategies that apply equally to all universities, rather than requiring customized approaches based on university type (Khan et al., 2024 ). Nevertheless, the study also revealed a statistically significant difference (p < 0.05) in access to AI tools between public and private universities, highlighting the deep disparities in resource distribution. This aligns with Chabalala et al. ( 2024 ), who observed that students and faculty in under-resourced institutions struggle to experience AI’s full benefits, leading to skepticism and reduced enthusiasm for its adoption. When universities lack the necessary tools, training, and digital infrastructure, it becomes difficult for educators to integrate AI into their teaching, thus limiting its potential impact (Hur, 2024 ). One of the key takeaways from this study is the urgent need for government intervention to support AI integration in Ugandan universities. Respondents across all institutions identified government funding for ICT infrastructure as one of the most important factors in AI adoption. In addition, many faculty members highlighted the lack of AI-related training programs as a significant barrier, with suggestions that mandatory AI workshops and faculty development programs should be introduced to increase adoption rates. This aligns with Moradi, H. ( 2025 ) and Gikunda ( 2023 ), stressed that capacity-building programs are essential for improving AI adoption among faculty and students. Moreover, ethical concerns regarding AI grading, student profiling, and algorithmic fairness were raised by several respondents. While AI can enhance efficiency in areas such as grading and administrative decision-making, concerns about bias, transparency, and data privacy must be addressed. Adıgüzel et al. ( 2023 ) found that AI models trained on biased datasets can unintentionally reinforce inequities in education, making it imperative that universities develop ethical guidelines and monitoring mechanisms to ensure AI use remains fair and accountable (Saran et al., 2023 ). Another significant finding of this study is the clear digital divide between public and private universities, with private institutions benefiting from better access to AI tools, more stable internet connectivity, and updated technology infrastructure. Meanwhile, public universities struggle with outdated ICT facilities, inconsistent electricity, and limited budget allocations for technology development. Research by Nguyen et al. ( 2023 ) emphasizes that such disparities in technology access can widen educational inequalities, leaving students and faculty at under-resourced institutions at a disadvantage. One way to address these challenges is through targeted investment in AI infrastructure at public universities. Government agencies, in partnership with the private sector, should prioritize expanding digital infrastructure in public universities, ensuring that all students and faculty have access to the necessary tools. Aithal and Maiya ( 2023 ) suggest that public-private partnerships can play a key role in distributing AI resources more equitably, enabling universities to pilot AI-driven solutions that fit their specific needs. There is also an opportunity to learn from international best practices. For example, in India and Kenya, public-private collaborations have successfully helped universities expand access to digital learning platforms and AI tools (Khan et al., 2024 ). Adopting similar models in Uganda could ensure that AI-driven education is inclusive and accessible to all students, regardless of socioeconomic background (Vaz, 2024 ). Conclusion The findings of this study reveal that while AI technologies are beginning to reshape administrative and academic processes in Ugandan universities, their integration, particularly in teaching and learning, remains fragmented and largely experimental. AI tools such as chatbots and automated systems are used more effectively in administrative functions, with students and staff acknowledging their role in improving communication and reducing workload. However, the adoption of AI for instructional purposes is still minimal, hindered by infrastructure deficiencies, limited training among faculty, and disparities in resource allocation between public and private universities. These challenges confirm that institutional preparedness significantly shapes both the usage and perception of AI’s effectiveness. Despite these obstacles, there is strong optimism among faculty, students, and administrators regarding AI’s potential to personalize learning, enhance student support, and increase institutional efficiency. This optimism underscores a critical opportunity: with strategic investment in digital infrastructure, mandatory AI training programs, and clear ethical policies, Ugandan universities can accelerate AI adoption in ways that are inclusive and sustainable. Moreover, addressing digital inequality through targeted policy interventions and public-private partnerships is vital to ensuring that all institutions, regardless of type or funding status, are able to harness AI’s transformative power. In doing so, AI will not only serve as a tool for operational efficiency but also as a driver of educational equity and innovation. Recommendations It is clear that meaningful AI integration in Ugandan universities cannot occur without deliberate action on several fronts. First and foremost, universities, especially public ones, need targeted investment in digital infrastructure. The widespread issues reported by faculty, students, and administrators, such as unreliable internet, outdated labs, and inconsistent access to devices, must be urgently addressed. Bridging this infrastructure gap is not just about hardware; it is about creating the foundational environment where AI can thrive. Second, the study reveals a strong willingness among faculty and administrators to adopt AI, but this enthusiasm is held back by a lack of training. Professional development programs focused on AI literacy, hands-on tool usage, and ethical awareness should be institutionalized and made mandatory across the board. Training should not be treated as an optional extra but as a core component of teaching and administrative capacity-building. Third, the enthusiasm for AI's potential, especially in enhancing administrative efficiency and student learning, should be harnessed through clear policy frameworks. These policies must guide ethical AI use, protect student data, and ensure fair deployment across different departments and institutions. Importantly, equity must be at the heart of all efforts. Universities with fewer resources should not be left behind as AI becomes a norm in higher education. Finally, collaboration between universities, government bodies, and technology providers will be essential for scaling AI in a sustainable and inclusive way. Such partnerships could open doors to funding, shared resources, and ongoing technical support. If these steps are taken thoughtfully, Uganda’s higher education sector stands to benefit immensely from the transformative potential of AI, making learning more personalized, administration more efficient, and education more accessible to all. Limitations While this study provides valuable insights into AI adoption in Ugandan higher education, it is important to acknowledge its limitations: The study primarily focused on two Ugandan universities, limiting generalizability to the entire higher education sector. Future research should expand the sample size to include more universities across different regions. The findings rely on self-reported perceptions from students, faculty, and administrators, which may introduce subjectivity and response bias. A more objective assessment of AI adoption levels using institutional data could strengthen the conclusions. AI is rapidly evolving, and the technologies assessed in this study may change or become obsolete in the near future. Future studies should continuously assess AI adoption trends to capture emerging developments. The study provides a snapshot of AI adoption at a specific point in time. A longitudinal approach tracking changes in AI adoption over time would offer a more comprehensive understanding of trends and challenges. While the study highlights concern about algorithmic bias and data privacy, it does not provide a detailed analysis of specific AI-related ethical challenges in Ugandan universities. Future research should investigate the ethical implications of AI adoption in education in greater depth. Declarations Funding No funding for this study Conflict of interest/competing interests There is no conflict of interest in this study Authors contributions The authors confirm contribution to the paper as follows: study conception and design : Michael Adelani Adewusi, Ola Tokunbo Odekeya; data collection, analysis and interpretation of results : Michael Adelani Adewusi, Ola Tokunbo Odekeya; draft manuscript preparation and proof-reading : Christine Ainebyoona. Final overall reading ; Muhammed Ngoma. 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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-6883202","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470741217,"identity":"01c893a1-1ed2-4002-9b3e-282a8e4fe6f0","order_by":0,"name":"MICHAEL ADELANI ADEWUSI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACZjYgwcZgx8bM2PiAgeEA8VqS+dmbDxsQp4UBooVxZs+xNAmitOi2syU++FFmw2xwI8esmqfmjhw/A/PDRzfwaDE7zHbYsOdcGh9Iy22eY8+MJRvYjI1z8Gphb5PgbTvMDNHCdjhxwwEeNmkCWtp//m37z7gBqKWY5x9RWtiOMfO2HQB7H8ggTkuytMy5ZHAgS87tO2ws2UzIL+ePGX58U2YHjsoPb74dlgPqffgYnxYUwMQDIpmJVQ4CjD9IUT0KRsEoGAUjBgAAtw9O4Gs28pUAAAAASUVORK5CYII=","orcid":"","institution":"Kampala International University","correspondingAuthor":true,"prefix":"","firstName":"MICHAEL","middleName":"ADELANI","lastName":"ADEWUSI","suffix":""},{"id":470741219,"identity":"81493b0e-3681-431a-8ed7-2b9fe8351dc7","order_by":1,"name":"CHRISTINE AINEBYOONA","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"CHRISTINE","middleName":"","lastName":"AINEBYOONA","suffix":""},{"id":470741220,"identity":"bf95aab7-0032-4a78-b089-be2f72e03daa","order_by":2,"name":"OLA TOKUNBO ODEKEYE","email":"","orcid":"","institution":"Osun State University","correspondingAuthor":false,"prefix":"","firstName":"OLA","middleName":"TOKUNBO","lastName":"ODEKEYE","suffix":""},{"id":470741221,"identity":"a29cb947-c96a-40d9-a54b-31d9668a14a5","order_by":3,"name":"MUHAMMED NGOMA","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"MUHAMMED","middleName":"","lastName":"NGOMA","suffix":""}],"badges":[],"createdAt":"2025-06-12 21:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6883202/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6883202/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104958284,"identity":"ee029e6b-09dc-4f4a-b279-742ac7fb7f53","added_by":"auto","created_at":"2026-03-19 08:27:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":881164,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6883202/v1/b14a3c2a-cbba-41a6-89c9-9c63adb114c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI in Universities: The Good, the Bot, and the Ugly Truths","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is rapidly reshaping university education, offering new ways to enhance teaching, learning, and administration (Zawacki-Richter et al 2024). It refers to the simulation of human intelligence by machines, particularly systems capable of learning, reasoning, and problem-solving (Clegg \u0026amp; Sarker, 2024). In educational contexts, AI encompasses tools such as intelligent tutoring systems, adaptive learning platforms, predictive analytics, and automated administrative technologies that enhance decision-making and personalize the learning experience. From adaptive learning platforms and predictive analytics to automated grading and personalized student support, AI holds the potential to create more responsive and efficient educational environments (Singh, 2023; Sajja et al., 2024). These advancements enable universities to analyze vast amounts of data in real time, allowing educators to tailor instruction to individual student needs and streamline administrative operations. However, the integration of AI into education is not without challenges, especially in developing countries where infrastructure, funding, and technical expertise remain limited (Singh, 2023).\u003c/p\u003e\n\u003cp\u003eMuch of the research and development in AI-powered education has concentrated on institutions in developed nations, where strong digital infrastructure and consistent funding facilitate the smooth deployment of AI tools (Yigitcanlar et al., 2024). In contrast, universities in sub-Saharan Africa, including Uganda, grapple with issues such as underfunding, unreliable internet connectivity, and a shortage of trained personnel (Atuahene \u0026amp; XuSheng, 2024; Bulathwela et al., 2024). These challenges not only slow AI adoption but also deepen existing inequalities in access to quality education.\u003c/p\u003e\n\u003cp\u003eTwo major areas where AI is transforming education globally are personalized learning and automated university administration. For instance, platforms like ALEKS and Knewton are helping students improve their performance by adapting content to each learner\u0026rsquo;s progress and style (Ahmad et al., 2025; Ayeni et al., 2024). Meanwhile, universities are also deploying AI-powered administrative systems, including chatbots, predictive analytics tools, and automated enrollment platforms, to streamline admissions, offer real-time student support, and reduce workload on staff (George \u0026amp; Wooden, 2023). While these innovations show great promise, they also raise ethical concerns related to data privacy, algorithmic fairness, and the reduction of human interaction in student services (Rane et al., 2024).\u003c/p\u003e\n\u003cp\u003eHowever, in Uganda, the integration of AI into university education remains highly uneven due to persistent structural and institutional challenges (Mukasa, 2024). One of the most pressing issues is unreliable internet connectivity, which continues to affect many universities, particularly public institutions and regional campuses. Internet speeds are often too slow to support AI-powered platforms, and outages are common during peak learning hours (Mukasa, 2024). Faculty training presents another significant obstacle. While lecturers express growing interest in leveraging AI to support student learning and streamline grading or feedback processes, most have not received specialized training or professional development in digital pedagogy or AI use (Nalubega \u0026amp; Uwizeyimana, 2024). These gaps are compounded by limited budgets, bureaucratic procurement systems, and the absence of clear national or institutional strategies guiding AI integration (Mukasa, 2024; Nalubega \u0026amp; Uwizeyimana, 2024). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo better understand these realities, this study examined two universities representing different institutional contexts, one public and one private. The public institution had begun integrating AI in selected administrative processes and limited teaching tools but faced obstacles such as over-enrollment and insufficient IT infrastructure. The private institution, on the other hand, showed greater readiness and engagement with AI applications, supported by more flexible funding and manageable student populations. This contrast provided a valuable lens through which to explore the broader challenges and opportunities of AI adoption in Uganda\u0026rsquo;s higher education landscape.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo guide this study, it draws upon two key theoretical models: the Technology Acceptance Model (TAM) and the Digital Divide Theory. TAM helps explain how perceived usefulness and ease of use influence the acceptance and integration of AI technologies among faculty and students (Pillai et al., 2024). Meanwhile, the Digital Divide Theory underscores how structural inequalities, such as infrastructure, funding, and digital literacy, can affect the equitable distribution and usage of AI in education (Memon \u0026amp; Memon, 2025). Together, these theories offer a foundation for analyzing the patterns, perceptions, and institutional readiness observed in the Ugandan higher education sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Purpose and Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of this study is to examine how artificial intelligence (AI) technologies are being adopted and perceived in university education in Uganda, with a focus on their role in teaching, learning, and administrative functions. It also aims to identify barriers and propose actionable strategies for inclusive, ethical, and sustainable AI integration in higher education settings.\u003c/p\u003e\n\u003cp\u003eTo achieve this, the study is guided by the following specific objectives:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eTo assess how AI technologies are currently being used in teaching, learning, and administrative operations in selected Ugandan universities.\u003c/li\u003e\n \u003cli\u003eTo evaluate the impact of AI-powered administrative tools on institutional efficiency and the delivery of student support services in these universities.\u003c/li\u003e\n \u003cli\u003eTo identify context-specific policy measures and strategic interventions that can promote equitable access, infrastructure development, and ethical use of AI in Uganda\u0026rsquo;s higher education system.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Questions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo achieve its objectives, the study is guided by the following research questions:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHow are AI technologies currently being used in teaching, learning, and administrative functions in Ugandan universities?\u003c/li\u003e\n \u003cli\u003eIn what ways have AI-powered administrative tools improved institutional efficiency and student support services?\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003eWhat specific policy measures and strategic interventions can support equitable and sustainable AI adoption in Ugandan universities?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eH\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e: There is no statistically significant difference in AI adoption levels between public and private universities in Uganda.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e: There is no statistically significant difference in perceived institutional efficiency between universities using AI-powered administrative tools and those that do not.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Literature Review","content":"\u003cp\u003eA study by Khoalenyane and Ajani (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), systematically reviewed AI-enhanced adaptive learning platforms and reported their potential to personalize learning. However, these tools were often underutilized due to infrastructural gaps and limited faculty expertise, issues that mirror Uganda\u0026rsquo;s own digital challenges. In the same vein, Maphalala and Ajani (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated how institutions implemented intelligent tutoring systems, plagiarism detectors, and content recommendation engines. These AI tools, when effectively integrated, improved student engagement, academic performance, and satisfaction by promoting a personalized and adaptive learning experience. These findings highlight the transformative potential of AI in enhancing educational delivery, while also emphasizing the practical limitations posed by insufficient digital infrastructure and capacity.\u003c/p\u003e \u003cp\u003eThe studies of Patel and Ragolane (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who investigated AI deployment in student registration systems, AI-driven helpdesks, and course planning tools. Their research revealed that these technologies significantly improved institutional responsiveness and efficiency by automating routine tasks and facilitating real-time support. Nevertheless, they also identified systemic barriers including socio-economic disparities, digital divides, and data-related ethical concerns. In another study, Goosen and Mugumo (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) proposed an AI integration framework for academic advising, admissions, and performance tracking. Their results confirmed that efficiency gains were most pronounced when ethical policies were in place, highlighting the need for governance mechanisms to accompany technological implementation.\u003c/p\u003e \u003cp\u003eChakraborty (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) cautioned against unchecked deployment of AI tools, documenting ethical dilemmas like algorithmic bias, lack of transparency, and student consent violations in adaptive learning systems. Further, Ajani, Gamede, and Matiyenga (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found low awareness of AI risks among faculty and students, reinforcing the urgency of targeted AI ethics training and rights-based digital literacy campaigns. To bridge these ethical and infrastructural gaps, Funda and Mbangeleli (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), along with Airaj (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), proposed policy reforms centered on expanding broadband infrastructure, strengthening faculty capacity in AI, and promoting collaboration between institutions. They argue that AI governance in African universities must be grounded in culturally relevant, inclusive policies to avoid reinforcing existing educational inequities. These insights collectively support this study\u0026rsquo;s aim to develop context-specific, ethical, and sustainable AI strategies for Uganda\u0026rsquo;s higher education sector.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study adopted a mixed-methods approach. As a pragmatic researcher, I believe that neither quantitative nor qualitative methods alone can adequately capture the multifaceted realities of AI integration in higher education, particularly in a context like Uganda\u0026rsquo;s, where institutional capacities and access to digital tools vary widely. Therefore, combining these methods was essential to obtaining a comprehensive understanding of the current state, perceived value, and potential equity-related implications of AI technologies.\u003c/p\u003e\n\u003cp\u003eThe research design was both explanatory and comparative, structured to explore the extent of AI use, analyze its impact on university administration and student services, and propose relevant interventions. This design made it possible to capture both broad statistical patterns and context-specific insights across two carefully selected universities, a public and a private institution. These were not named for ethical reasons but were chosen based on their differing levels of digital maturity, infrastructure, and resource availability, making them ideal for a comparative analysis of AI adoption in diverse settings.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe target population included students, faculty members, and administrators actively involved in teaching, learning, or administrative functions. These groups were selected due to their direct interaction with AI systems, whether through classroom instruction, digital service delivery, or institutional policy development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA stratified sampling technique was used for selecting students and faculty to ensure representation across academic faculties and departments, while purposive sampling identified administrators and IT personnel with direct exposure to AI tools.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1: Summary of the study population and sampling\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParticipant Category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePopulation Estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSample Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSampling Method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e~5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStratified Random Sampling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty Members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e~300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStratified Random Sampling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e~50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePurposive Sampling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData Collection Instruments were designed to directly reflect the three study objectives:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eA structured questionnaire was administered to students and faculty. It included closed-ended and Likert-scale items capturing awareness, usage patterns, perceived effectiveness, and ethical concerns regarding AI.\u003c/li\u003e\n \u003cli\u003eSemi-structured interviews were conducted with administrative and IT staff to gain insights into institutional practices, resource barriers, and policy perspectives. These allowed for deeper exploration of qualitative themes.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo ensure data quality, the questionnaire was reviewed by experts to establish content and face validity. Reliability was assessed using Cronbach\u0026rsquo;s alpha for internal consistency. For the qualitative component, trustworthiness was enhanced through triangulation, member checking, and use of NVivo for systematic coding and thematic analysis.\u003c/p\u003e\n\u003cp\u003eIn terms of data management, quantitative data was entered, cleaned, and analyzed using SPSS Version 25. Descriptive statistics (means, standard deviation, frequencies) described AI usage trends, while ANOVA tested for differences in perceptions between public and private university contexts. The qualitative interview transcripts were analyzed using thematic analysis, generating rich categories on AI implementation, challenges, and policy gaps.\u003c/p\u003e\n\u003cp\u003eFinally, ethical standards were rigorously maintained. Participants provided informed consent, and their responses were anonymized to ensure confidentiality. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eThematic Analysis of Coded Responses\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question 1\u003c/strong\u003e: How are Ugandan universities currently using AI in teaching, learning, and administration?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1: AI in teaching, learning, and administration\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 195px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty staff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdministrators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1. Perceptions of AI Integration in Teaching and Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 195px;\"\u003e\n \u003cp\u003eMany faculty members feel that AI tools are still a long way from becoming part of everyday teaching. As one lecturer explained, while tools like Grammarly and Turnitin are helpful, they are mostly used for writing support, not actual instruction. There\u0026apos;s a sense that more training is needed before AI can be used more meaningfully in classrooms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eStudents notice that some lecturers are trying to integrate AI-based platforms like Google Classroom and Moodle, which offer a bit of personalization. But they also feel this is not consistent across courses or departments. Some students benefit from it, while others do not even get to see those tools in use.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eFrom the administrative side, there is acknowledgment that AI has not yet taken hold in teaching. Despite investments in digital learning platforms, administrators say many lecturers still rely on traditional methods, partly due to a lack of comfort or familiarity with AI tools.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2. Use of AI in Administrative and Support Functions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 195px;\"\u003e\n \u003cp\u003eFaculty generally see administrative AI as a relief. One lecturer pointed out that the chatbot used by the university has taken a load off their shoulders by answering student questions, which allows them to focus more on teaching and mentoring.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eStudents appreciate how AI helps them get faster responses from the university. Whether it is about their schedules, admissions, or campus queries, they find chatbots convenient and efficient. For many, it reduces the long queues and delays they used to experience.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eAdministrators are proud of how AI is improving service delivery. They shared that chatbots and AI dashboards have helped streamline tasks like admissions, inquiries, and records management. It makes their work more efficient and supports smoother campus operations.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3. Challenges Hindering Effective AI Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 195px;\"\u003e\n \u003cp\u003eFaculty express frustration with infrastructure limitations. Many want to use AI tools but are held back by unstable internet, lack of computers, and limited exposure to how these tools work. One lecturer said \u0026lsquo;\u003cem\u003ethey\u0026rsquo;d be more open to AI if proper training and tech support were available\u0026rsquo;\u003c/em\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eStudents feel the same pinch. Some have access to digital learning platforms, while others do not. A student explained that it often depends on the lecturer and the department, \u0026lsquo;\u003cem\u003esome are equipped, others are not\u0026rsquo;\u003c/em\u003e. This creates a feeling of inequality among students.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eAdministrators say the challenges go deeper. They talk about budget limitations, power outages, and old systems that are hard to upgrade. Even when AI tools are introduced, sustaining them is a real challenge without reliable infrastructure and consistent funding.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4. Shared Vision and Opportunities for AI in Higher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 195px;\"\u003e\n \u003cp\u003eDespite the hurdles, faculty are hopeful. One lecturer shared that \u0026lsquo;\u003cem\u003ethey\u0026rsquo;d love to use AI more, especially to track student progress and provide tailored support, if they could get the right kind of training and tools\u0026rsquo;\u003c/em\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eStudents are equally enthusiastic. They believe AI could help them learn better, especially when it adapts to their pace and gives them quicker, clearer feedback. Many see it as a step toward a more student-centered learning experience.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eAdministrators also have a positive outlook. They see AI as a powerful tool for improving how the university operates, from data analysis to student retention. They believe that with the right investments and policies, AI can transform the entire educational experience.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2: Description statistics on how AI technologies are currently being used\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvey Item\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% of Agreement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI use AI tools in teaching and assessment (e.g., Turnitin, Moodle AI tools).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAI is integrated into our learning platforms for personalised instruction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOur university uses chatbots or AI-driven systems for admin tasks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInfrastructure limitations hinder AI adoption in teaching and learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty are adequately trained to implement AI in academic delivery.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation:\u0026nbsp;\u003c/strong\u003eWhile the current use in teaching and learning remains limited, primarily involving basic tools like Turnitin and Grammarly, there is a noticeable shift toward more impactful applications in administrative functions, particularly through chatbots and digital dashboards that have improved communication and efficiency. Students and faculty highlight the inconsistent use of learning platforms such as Moodle and Google Classroom, and only 39% of faculty feel adequately trained to implement AI effectively. Infrastructure remains a major concern, with 88% of respondents citing unreliable internet and outdated equipment as key barriers. However, the overall sentiment across faculty, students, and administrators is one of cautious optimism. With increased investment in digital infrastructure and targeted training programs, stakeholders believe that AI holds transformative potential to make education in Uganda more personalized, efficient, and inclusive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question 2\u003c/strong\u003e: How do institutional resources and access to AI tools influence perceptions of its effectiveness?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3: Institutional resources and AI tools perceptions\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"701\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty staff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdministrators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResource Availability Shapes AI Perceptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty staff in the public institutions often feel left behind due to limited access to AI tools. One faculty member noted, \u003cem\u003e\u0026ldquo;In private universities, there\u0026rsquo;s visible investment in technology. Here, we\u0026rsquo;re still struggling with access to functional computers, so AI seems out of reach.\u0026rdquo;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents, too, notice the contrast between institutions. Many shared that while peers in better-resourced universities use AI daily, they are stuck in outdated labs or manually managed learning environments. This inequality influences how they perceive AI\u0026rsquo;s usefulness.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators acknowledge that resource disparities are a major roadblock. They admit that without the proper infrastructure like the reliable internet, modern systems, and enough devices, AI cannot be effectively adopted, especially in public universities operating under tight budgets.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDigital Divide Between Universities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty in underfunded universities feel that the digital divide is growing. One lecturer shared, \u003cem\u003e\u0026ldquo;We see what\u0026rsquo;s possible elsewhere, but we\u0026rsquo;re barely getting by with old projectors and slow internet. AI feels like a luxury.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents highlighted how the divide is real and frustrating. As one put it, \u003cem\u003e\u0026ldquo;We hear about AI tools, but in our labs, half the computers don\u0026rsquo;t even turn on. It\u0026rsquo;s hard to be excited about tech we can\u0026rsquo;t use.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators explained that institutional type directly affects AI readiness. They pointed out that private institutions often have flexible funding structures, while public ones face bureaucratic hurdles, making it harder to keep up with AI trends.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of Training Affects Perceived AI Effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty agree that even when tools are available, they are not always confident using them. \u003cem\u003e\u0026ldquo;AI sounds great in theory, but we\u0026rsquo;ve never been trained. Without proper exposure, it\u0026rsquo;s hard to see how it fits into our teaching,\u0026rdquo;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents notice that some of their lecturers shy away from digital tools. One student commented, \u003cem\u003e\u0026ldquo;Sometimes, the tools are there, but the lecturer doesn\u0026rsquo;t use them properly. I think it\u0026rsquo;s because they haven\u0026rsquo;t been trained.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators admitted that training has not kept pace with AI adoption. They noted that many of their staff are willing to learn but need structured workshops and consistent support to apply AI tools effectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAI Perceived as a Tool for Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty who has managed to experiment with AI tools find them helpful. One said, \u003cem\u003e\u0026ldquo;Automated grading saves me hours every week, but unfortunately, not everyone in the department has access to these tools.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents appreciate how AI helps streamline processes. \u003cem\u003e\u0026ldquo;I get quicker feedback on my assignments now, and accessing course materials is easier,\u0026rdquo;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators are clear about AI\u0026rsquo;s operational benefits. \u003cem\u003e\u0026ldquo;AI chatbots and data dashboards have made it easier to handle student inquiries and analyze trends. But cost is a barrier. Only a few departments have these tools,\u0026rdquo;\u003c/em\u003e an administrator explained.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 4: Description statistics on institutional resources and access to AI tools influence perceptions of its effectiveness\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvey Item\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Agreement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAI has reduced the administrative workload for staff.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents receive timely support through AI chatbots or digital assistants.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOur admissions and registration systems benefit from AI tools.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAI tools in administration have increased transparency and efficiency.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe use of AI for institutional management is still limited to pilot programs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation:\u003c/strong\u003e Across faculty, students, and administrators, there is a shared concern that inadequate infrastructure, especially in public institutions, is holding back meaningful AI adoption. Faculty feel frustrated by the lack of access to functioning computers and stable internet, while students point to outdated labs and limited exposure to digital tools. Administrators confirm that budget constraints and slow decision-making processes continue to widen the digital divide. These limitations have created a perception of AI as more of a future aspiration than a current solution. Even when AI tools are introduced, many faculty feel underprepared, and students observe that lecturers often lack the training to use them effectively. Administrators agree that professional development hasn\u0026apos;t kept pace with the rollout of AI tools. Still, where AI has been implemented, such as in automated grading and chatbots, there is strong agreement that these tools improve efficiency. Survey data supports this, with 75% of respondents acknowledging reduced administrative workload and 79% recognizing improved admissions systems. However, the fact that 83% still see AI tools as limited to pilot phases underscores a gap between recognition of AI\u0026rsquo;s benefits and its full-scale implementation. To bridge this gap, the study points to a clear need for targeted investments in both infrastructure and staff training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question 3:\u003c/strong\u003e What policy measures can support AI-driven innovation while ensuring equitable access?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 5: Policy measures to support AI\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaculty staff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdministrators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeed for Government Funding \u0026amp; Infrastructure Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty members emphasize that without significant infrastructure upgrades, AI cannot be effectively integrated into teaching. One faculty respondent shared, \u003cem\u003e\u0026ldquo;We have the desire, but not the tools. Until the government invests in ICT infrastructure, we\u0026rsquo;ll remain limited to theory.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents echo the call for government support, pointing out that their learning environment is constrained by outdated or inaccessible technology. One student said, \u003cem\u003e\u0026ldquo;We can\u0026rsquo;t talk about AI if the computers barely work. We need help from higher up.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators are vocal about this need, too. They believe the government should prioritize ICT development across institutions, not just a select few. \u003cem\u003e\u0026ldquo;Without public investment in digital infrastructure,\u0026rdquo;\u003c/em\u003e one stated, \u003cem\u003e\u0026ldquo;AI will remain a buzzword, not a practice.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMandatory AI Training for Faculty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty see training as a critical enabler. As one lecturer put it, \u003cem\u003e\u0026ldquo;We\u0026rsquo;re not resistant to AI, we\u0026rsquo;re just not trained. If workshops were part of our professional development, we\u0026rsquo;d adopt AI more confidently.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents have noticed that some lecturers are hesitant or inconsistent in using AI tools. \u003cem\u003e\u0026ldquo;It\u0026rsquo;s not that they don\u0026rsquo;t care,\u0026rdquo;\u003c/em\u003e one student explained, \u003cem\u003e\u0026ldquo;They just haven\u0026rsquo;t been shown how.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators recognise that AI training must be institutionalised. They agree that faculty development programmes must evolve to include AI competencies. \u003cem\u003e\u0026ldquo;It shouldn\u0026rsquo;t be optional anymore,\u0026rdquo;\u003c/em\u003e one administrator emphasised.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEquitable AI Resource Allocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty in public institutions stress that AI integration should not be a privilege of private universities. \u003cem\u003e\u0026ldquo;Our students deserve the same access,\u0026rdquo;\u003c/em\u003e one said. \u003cem\u003e\u0026ldquo;The government and university management must address this gap.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents from underfunded universities feel left behind. One said, \u003cem\u003e\u0026ldquo;It\u0026rsquo;s not fair that others get personalized AI tutors while we struggle with old equipment. We need equal access if we\u0026rsquo;re all expected to compete globally.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators call for policy-driven, equitable distribution of digital resources. \u003cem\u003e\u0026ldquo;Public universities need prioritised support,\u0026rdquo;\u003c/em\u003e one explained, \u003cem\u003e\u0026ldquo;so students from less privileged backgrounds can benefit just as much as those in elite institutions.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePolicy Guidelines for Ethical AI Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFaculty are deeply concerned about the ethics of AI in education. One noted, \u003cem\u003e\u0026ldquo;AI must not become a tool for unfair grading or profiling. Clear rules and oversight are essential to protect students.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudents worry about how their data is used. \u003cem\u003e\u0026ldquo;I want AI to help me, not judge me,\u0026rdquo;\u003c/em\u003e one student said. \u003cem\u003e\u0026ldquo;There should be rules so it doesn\u0026rsquo;t become invasive or biased.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdministrators agree that strong ethical policies are needed. \u003cem\u003e\u0026ldquo;We need institutional frameworks that safeguard privacy, ensure fairness, and build trust among users,\u0026rdquo;\u003c/em\u003e one administrator stated.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 6: Description statistics on specific policy measures and strategic interventions\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"648\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvey Item\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% on Agreement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eThe government should invest more in digital infrastructure for universities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eAI training should be mandatory for academic staff.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eUniversities need clear policies on data privacy and AI ethics.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003ePublic-private partnerships can help scale AI implementation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eEquity in AI access between public and private universities is a key priority.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation:\u0026nbsp;\u003c/strong\u003eFindings reveal a shared urgency among faculty, students, and administrators for robust policy measures to support AI adoption in Ugandan universities. Participants widely agree that without substantial government investment in digital infrastructure, the integration of AI will remain limited to a privileged few institution. Faculty express frustration at the gap between their willingness to use AI and the lack of available tools; students point to outdated equipment and under-resourced labs; while administrators emphasize that broad, inclusive government funding is essential for meaningful, system-wide progress. This collective sentiment is reinforced by the data, with 91% of respondents calling for infrastructure investment and 90% stressing the importance of equitable access. Additionally, the findings highlight that lack of training, not lack of interest, is a key barrier. Faculty, students, and administrators alike support making AI training mandatory, which aligns with the 88% of respondents who endorsed this idea. Ethical concerns also feature prominently: 89% of participants agree on the need for clear policies to safeguard data privacy and prevent algorithmic bias. Moreover, the support for public-private partnerships (85%) suggests that sustainable AI adoption will require a collaborative effort combining policy, training, and cross-sector investment to ensure all universities, regardless of resources, can participate in and benefit from AI advancements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eResearch hypotheses\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch hypothesis 1:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThere is no statistically significant difference in AI adoption levels between public and private universities in Uganda.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 7: ANOVA analysis on AI adoption levels between public and private universities in Uganda\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e300.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e302.133\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e165\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation:\u003c/strong\u003e There is no statistically significant difference in AI adoption levels between public and private universities in Uganda [F (1, 164) = 0.733; p \u0026gt; 0.05].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis result indicated that both students and the lecturers from the universities (public and private) perceived the AI adoption similarly with no differences among them. This further implies that any efforts to promote the adoption of AI can be directed uniformly across the universities, as students and lecturers share similar views about AI\u0026apos;s potential benefits.\u003c/p\u003e\n\u003cp\u003eResearch hypothesis 2:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThere is no statistically significant difference in perceived institutional efficiency between universities using AI-powered administrative tools and those that do not.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 8: ANOVA analysis on faculty and students in universities that use AI-powered administrative tools\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e461.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e480.167\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e165\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eInterpretation: There is a statistically significant difference between faculty and students in universities that use AI-powered administrative tools that report higher satisfaction with institutional processes than those in universities without such tools (p \u0026lt; 0.05). This indicates disparities in access across institutions.\u003c/p\u003e\n\u003cp\u003eThis result implies that there is or are disparities in how public and private universities provide or utilize AI tools which could be due to differences in funding, infrastructure, or institutional priorities. The limited access to AI tools in the public institutions, may negatively affect how students and faculty perceive AI\u0026rsquo;s effectiveness. The result shows the importance of equitable access to AI tools as a key determinant of how effectively institutions can integrate AI into education.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from this study reveal that while AI adoption in Ugandan universities is gradually increasing, it is still largely confined to administrative functions and plagiarism detection tools, rather than core teaching and learning activities. Many faculty members and students reported that tools like Grammarly and Turnitin were commonly used to assist in academic writing and plagiarism detection. However, fully adaptive AI-powered learning systems remain largely underutilized, with only a few lecturers using platforms like Google Classroom and Moodle to personalize assignments. These findings align with research by McGrath et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who noted that AI has tremendous potential to personalize learning, enhance administrative efficiency, and improve student engagement, yet its full implementation in educational institutions remains a challenge.\u003c/p\u003e \u003cp\u003eOne of the major obstacles to AI integration in Ugandan universities is the lack of adequate digital infrastructure and technical training for faculty members. Respondents noted that many lecturers had limited exposure to AI technologies, leading to reluctance or uncertainty in adopting AI-based teaching methods. Furthermore, public institutions, in particular, face challenges such as outdated computer labs, unstable internet access, and inadequate technical support, making it difficult to implement AI tools effectively. Heaton et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found similar challenges in other developing regions, where private universities, with more financial flexibility, tend to adopt cutting-edge educational technologies more readily than public institutions. If Ugandan universities hope to fully embrace AI-driven learning, it is essential to invest in infrastructure, offer continuous faculty training, and develop institutional policies that support AI integration (Gikunda, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study also underscores the critical role of institutional resources in shaping perceptions of AI effectiveness. The findings suggest that respondents from private universities have a more favorable view of AI, largely because they have better access to AI tools and infrastructure. In contrast, respondents from public universities expressed concerns that the limited availability of AI technologies makes them less relevant in their academic environment. This aligns with research by Heaton et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which highlights that institutions with stronger financial backing tend to integrate AI more effectively than those with limited funding.\u003c/p\u003e \u003cp\u003eHowever, despite differences in access to AI tools, this study found no significant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in how students and faculty perceive AI\u0026rsquo;s potential benefits across public and private universities. This suggests that the value of AI is widely recognized, regardless of institutional type. McGrath et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) similarly found that faculty and students across different educational institutions acknowledged AI\u0026rsquo;s transformative potential, even in cases where access was limited. This is an important finding because it means that policymakers can implement AI adoption strategies that apply equally to all universities, rather than requiring customized approaches based on university type (Khan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, the study also revealed a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in access to AI tools between public and private universities, highlighting the deep disparities in resource distribution. This aligns with Chabalala et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who observed that students and faculty in under-resourced institutions struggle to experience AI\u0026rsquo;s full benefits, leading to skepticism and reduced enthusiasm for its adoption. When universities lack the necessary tools, training, and digital infrastructure, it becomes difficult for educators to integrate AI into their teaching, thus limiting its potential impact (Hur, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the key takeaways from this study is the urgent need for government intervention to support AI integration in Ugandan universities. Respondents across all institutions identified government funding for ICT infrastructure as one of the most important factors in AI adoption. In addition, many faculty members highlighted the lack of AI-related training programs as a significant barrier, with suggestions that mandatory AI workshops and faculty development programs should be introduced to increase adoption rates. This aligns with Moradi, H. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Gikunda (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), stressed that capacity-building programs are essential for improving AI adoption among faculty and students.\u003c/p\u003e \u003cp\u003eMoreover, ethical concerns regarding AI grading, student profiling, and algorithmic fairness were raised by several respondents. While AI can enhance efficiency in areas such as grading and administrative decision-making, concerns about bias, transparency, and data privacy must be addressed. Adıg\u0026uuml;zel et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that AI models trained on biased datasets can unintentionally reinforce inequities in education, making it imperative that universities develop ethical guidelines and monitoring mechanisms to ensure AI use remains fair and accountable (Saran et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother significant finding of this study is the clear digital divide between public and private universities, with private institutions benefiting from better access to AI tools, more stable internet connectivity, and updated technology infrastructure. Meanwhile, public universities struggle with outdated ICT facilities, inconsistent electricity, and limited budget allocations for technology development. Research by Nguyen et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasizes that such disparities in technology access can widen educational inequalities, leaving students and faculty at under-resourced institutions at a disadvantage.\u003c/p\u003e \u003cp\u003eOne way to address these challenges is through targeted investment in AI infrastructure at public universities. Government agencies, in partnership with the private sector, should prioritize expanding digital infrastructure in public universities, ensuring that all students and faculty have access to the necessary tools. Aithal and Maiya (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggest that public-private partnerships can play a key role in distributing AI resources more equitably, enabling universities to pilot AI-driven solutions that fit their specific needs.\u003c/p\u003e \u003cp\u003eThere is also an opportunity to learn from international best practices. For example, in India and Kenya, public-private collaborations have successfully helped universities expand access to digital learning platforms and AI tools (Khan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Adopting similar models in Uganda could ensure that AI-driven education is inclusive and accessible to all students, regardless of socioeconomic background (Vaz, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study reveal that while AI technologies are beginning to reshape administrative and academic processes in Ugandan universities, their integration, particularly in teaching and learning, remains fragmented and largely experimental. AI tools such as chatbots and automated systems are used more effectively in administrative functions, with students and staff acknowledging their role in improving communication and reducing workload. However, the adoption of AI for instructional purposes is still minimal, hindered by infrastructure deficiencies, limited training among faculty, and disparities in resource allocation between public and private universities. These challenges confirm that institutional preparedness significantly shapes both the usage and perception of AI\u0026rsquo;s effectiveness.\u003c/p\u003e \u003cp\u003eDespite these obstacles, there is strong optimism among faculty, students, and administrators regarding AI\u0026rsquo;s potential to personalize learning, enhance student support, and increase institutional efficiency. This optimism underscores a critical opportunity: with strategic investment in digital infrastructure, mandatory AI training programs, and clear ethical policies, Ugandan universities can accelerate AI adoption in ways that are inclusive and sustainable. Moreover, addressing digital inequality through targeted policy interventions and public-private partnerships is vital to ensuring that all institutions, regardless of type or funding status, are able to harness AI\u0026rsquo;s transformative power. In doing so, AI will not only serve as a tool for operational efficiency but also as a driver of educational equity and innovation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eIt is clear that meaningful AI integration in Ugandan universities cannot occur without deliberate action on several fronts. First and foremost, universities, especially public ones, need targeted investment in digital infrastructure. The widespread issues reported by faculty, students, and administrators, such as unreliable internet, outdated labs, and inconsistent access to devices, must be urgently addressed. Bridging this infrastructure gap is not just about hardware; it is about creating the foundational environment where AI can thrive.\u003c/p\u003e \u003cp\u003eSecond, the study reveals a strong willingness among faculty and administrators to adopt AI, but this enthusiasm is held back by a lack of training. Professional development programs focused on AI literacy, hands-on tool usage, and ethical awareness should be institutionalized and made mandatory across the board. Training should not be treated as an optional extra but as a core component of teaching and administrative capacity-building.\u003c/p\u003e \u003cp\u003eThird, the enthusiasm for AI's potential, especially in enhancing administrative efficiency and student learning, should be harnessed through clear policy frameworks. These policies must guide ethical AI use, protect student data, and ensure fair deployment across different departments and institutions. Importantly, equity must be at the heart of all efforts. Universities with fewer resources should not be left behind as AI becomes a norm in higher education.\u003c/p\u003e \u003cp\u003eFinally, collaboration between universities, government bodies, and technology providers will be essential for scaling AI in a sustainable and inclusive way. Such partnerships could open doors to funding, shared resources, and ongoing technical support. If these steps are taken thoughtfully, Uganda\u0026rsquo;s higher education sector stands to benefit immensely from the transformative potential of AI, making learning more personalized, administration more efficient, and education more accessible to all.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights into AI adoption in Ugandan higher education, it is important to acknowledge its limitations:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe study primarily focused on two Ugandan universities, limiting generalizability to the entire higher education sector. Future research should expand the sample size to include more universities across different regions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe findings rely on self-reported perceptions from students, faculty, and administrators, which may introduce subjectivity and response bias. A more objective assessment of AI adoption levels using institutional data could strengthen the conclusions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAI is rapidly evolving, and the technologies assessed in this study may change or become obsolete in the near future. Future studies should continuously assess AI adoption trends to capture emerging developments.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe study provides a snapshot of AI adoption at a specific point in time. A longitudinal approach tracking changes in AI adoption over time would offer a more comprehensive understanding of trends and challenges.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhile the study highlights concern about algorithmic bias and data privacy, it does not provide a detailed analysis of specific AI-related ethical challenges in Ugandan universities. Future research should investigate the ethical implications of AI adoption in education in greater depth.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding for this study\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest in this study\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm contribution to the paper as follows: \u003cu\u003estudy conception and design\u003c/u\u003e: Michael Adelani Adewusi, Ola Tokunbo Odekeya; \u003cu\u003edata collection, analysis and interpretation of results\u003c/u\u003e: Michael Adelani Adewusi, Ola Tokunbo Odekeya; \u003cu\u003edraft manuscript preparation and proof-reading\u003c/u\u003e: Christine Ainebyoona. \u003cu\u003eFinal overall reading\u003c/u\u003e; Muhammed Ngoma. 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Smart Cities, 7(4), 1576-1625.\u003c/li\u003e\n\u003cli\u003eZawacki-Richter, O., Bai, J. Y., Lee, K., Slagter van Tryon, P. J., \u0026amp; Prinsloo, P. (2024). New advances in artificial intelligence applications in higher education? International Journal of Educational Technology in Higher Education, 21(1), 32.\u003c/li\u003e\n\u003cli\u003eZhang, S., \u0026amp; Leiringer, R. (2023). Closing capability gaps for procuring infrastructure public-private partnerships: A case study in China. International Journal of Project Management, 41(6), 102497.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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, Higher Education, Adaptive Learning, AI Adoption, Educational Technology, AI Ethics","lastPublishedDoi":"10.21203/rs.3.rs-6883202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6883202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) is reshaping higher education, offering opportunities for personalized learning, enhanced efficiency, and improved campus management. However, its adoption also presents challenges such as ethical concerns, disparities in access, and over-reliance on technology. This study explores the benefits and challenges of AI integration in two Ugandan universities, focusing on its impact on teaching, learning, and institutional administration. Using a mixed-methods approach, data were collected through surveys from both students (123) and faculty members (43) across some Ugandan universities, complemented by interviews. Quantitative data were analyzed using ANOVA to assess differences in perceptions of AI adoption, while qualitative data provided deeper insights into concerns surrounding data privacy, infrastructure limitations, and faculty readiness. The findings revealed significant disparities in AI adoption, with institutions possessing better resources and access to AI tools reporting more positive perceptions of its effectiveness. The study also highlighted concerns over unequal access to AI-driven educational tools, emphasizing the need for targeted policy interventions. Tabular presentations illustrated variations in AI adoption levels, showcasing both the potential and challenges faced by different institutions. Qualitative insights underscored fears of reliance on AI at the expense of human interaction, as well as the necessity for data protection measures. The study concluded that while AI adoption in Ugandan universities is still at a nascent stage, there is a strong shared recognition among students, faculty, and administrators of its potential to enhance teaching, learning, and administrative efficiency, yet significant barriers such as limited infrastructure, unequal access to AI tools, and insufficient training hinder its widespread implementation. The study recommends the strategic investments in digital infrastructure to ensure equitable AI access, the development of policies that prioritize inclusivity, and regular training programs for faculty and administrators to enhance AI literacy. Universities must balance AI adoption with ethical considerations, ensuring that technological advancements do not exacerbate educational inequalities.\u003c/p\u003e","manuscriptTitle":"AI in Universities: The Good, the Bot, and the Ugly Truths","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 05:36:00","doi":"10.21203/rs.3.rs-6883202/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":"45297f59-ecf1-4383-bdfb-51bc096fc8ad","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T08:26:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 05:36:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6883202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6883202","identity":"rs-6883202","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}