Samuel Ocen1*
Joseph Elasu
Sylvia Manjeri Aarakit
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- 1Department of Energy Science and Technology, Faculty of Energy Economics and Management Science, Makerere University Business School, Kampala, Uganda
- 2Department of Entrepreneurship and Innovation, Faculty of Entrepreneurship and Small Business Management, Makerere University Business School, Kampala, Uganda
- 3Department of Information Systems, Faculty of Computing and Informatics, Makerere University Business School, Kampala, Uganda
Artificial intelligence is revolutionizing industries including institutions of higher learning as it enhances teaching and learning processes, streamline administrative tasks and drive innovations. Despite the unprecedented opportunities, AI tools if not used correctly, can be challenging in education institutions. The purpose of this study was to comprehensively review the AI innovations, opportunities and challenges associated with the use of AI in higher Education of learning. A systematic literature review methodology was adopted and used to locate and select existing studies, analyze and synthesize the evidence to arrive at clear conclusion about the current debate in the area of study. Following the PRISMA, the study analyzed a total of 54 documents that met the inclusion and exclusion criteria set for selection of the documents. The review unveiled many opportunities including enhanced research capabilities, automation of administrative tasks among others. Artificial Intelligence tools are found to refine and streamline the administrative tasks in different units in higher institutions of learning. The challenges include ethical concerns, integrity issues and data fabrication issues. With the challenges notwithstanding, the benefits of Artificial Intelligence cannot be over emphasized. Artificial intelligence remains a powerful tool for research, automation of administrative tasked, personalized learning, inclusivity and accessibility of educational content for all. Emphasis should be put in regulatory frameworks detailing how such tools can be used while maintaining the level of ethical standards required.
1 Introduction
Technology and Artificial Intelligence (AI) is revolutionizing industries including institutions of higher learning. In education institutions of higher learning, artificial intelligence presents unprecedented opportunities to enhance learning experiences, streamline administrative tasks and drive innovations. Artificial intelligence tools are widely used to enhance teaching and learning process in higher institutions of learning. Tools such as intelligent tutoring systems provide personalized, adaptive learning experiences to students. They can also be used to assess students’ current knowledge level, identify gaps and adapt the learning content according Other tools such as Natural Language Processing allow computers to understand, interpret and generate human languages. As a result, chatbots that answer students’ questions and give feedback to students regarding questions, assignments and facilitate discussions on online forums has been developed.
However, it is also important to note that the artificial tools if not used correctly can be challenging in education institutions. While Artificial Intelligence tools can play a significant role in helping students with their writing, there is evidence that over reliance on Artificial Intelligence tools by students contributes significantly to the loss of creativity and moral issues (Liang, 2023). Liang further notes that Artificial Intelligence makes suggestions that are contrary to social ethics and law. The development and deployment of artificial intelligence requires access to detailed data. The need for detailed data especially in education institution could easily affect data privacy, and security. As Artificial Intelligence models are not generally developed in consideration of educational usage or student privacy, the educational application of these models may not be aligned with the educational institution’s efforts to comply governing laws. There is death of scholarly work on the innovation, challenges, and opportunities regarding the use of artificial intelligence tools in education institutions of higher learning unfortunately, little has been done to profile these studies and use them for policy decisions.
As early as 1950, Allan Turing defined proposed the Turing Test to provide satisfactory operational definition of intelligence (Turing, 1950). According to the Turing test, intelligence is the machine’s ability to exhibit a behavior indistinguishable from that of a human being when engaged in natural language conversation (Turing, 1950; Stuart Russell, 2010). The art of creating machines that perform functions that require intelligence when performed by people is referred to as artificial intelligence (Gignac and Szodorai, 2024). Artificial intelligence is about the system’s ability to recognize patterns quantifiable through the observable development of actions or responses while achieving the complex goals in the complex environment (Goertzel, 2014). Simply put, artificial intelligence is the automation of as a way of automating activities that are associated with human thinking, such as decision making, problem solving, learning among others (Goertzel, 2014).
Conducting literature review on the status of artificial intelligence in terms of innovations, challenges and opportunities in education institutions of higher learning offers an opportunity to summaries, synthesize the arguments and ideas of existing knowledge on artificial intelligence and the opportunities it offers and challenges. In this study, a systematic literature review approach is adopted to systematically identify, evaluate and synthesize literature. The findings from this study provide useful insights in designing policies to guide on the use of artificial intelligence tools in education institutions of higher learning.
2 Methodology
This study follows the common approach of a systematic literature review suggested by Denyer and Tranfield (2009). This approach sharpens specific methodology of literature review and provides clear instructions for locating and selecting existing studies, analyze and synthesize data to arrive at clear conclusion about the current debate in the area of study (Denyer and Tranfield, 2009; Fraske, 2022). The following five procedural steps (see Figure 1) for systematic review defined by Denyer and Tranfield (2009) were followed: question formulation, locating studies, study selection and evaluation, analysis and evaluation, reporting and using results. Although there are biases associated with systematic literature approach for example, using limiters (time, data bases and journal restrictions), broadening the perspective in terms of wide time frame, i.e., 2000–2024 and using more than one data base made it possible to capture a wide number of the articles to answer the study.
Figure 1
Figure 1. Steps in conducting systematic reviews (Denyer and Tranfield, 2009).
2.1 Research steps
2.1.1 Step 1: Question formulation
To be able to gain an understanding and knowledge on technology readiness frameworks, the following questions are formulated to guide the study.
1. What Artificial Intelligence tools are majorly used in higher institutions of learning?
2. What opportunities exist for Artificial Intelligence in higher institutions of learning?
3. What challenges can be paused by the increasing use of Artificial Intelligence tools in Education institutions of higher learning?
2.1.2 Step 2: Locating studies
It is particularly of prime importance to ensure that the right records and or articles are selected for systematic literature review. To be able to achieve the objective of this study, data was collected from reputable databases and journals. The databases considered for data collection included Science direct, Emerald and other sources (Google Scholar and Google General). In selecting the databases, emphasis was put on those databases that provide metadata and abstracts: Metadata includes information on; year of publication, journal title, volume and Digital Objective Identifier (DOI) number. Secondly, the databases must have wide coverage of peer-reviewed academic literature. The choice for emerald and Science direct was due to the fact that they are most extensively used databases in literature search and most of the bibliometric analysis use these data bases for their search (Aghaei Chadegani et al., 2013; Mongeon and Paul-Hus, 2016). Other search engines, especially Google scholar, were considered and used during the search process.
Regarding journal selection, emphasis was put on the major leading journals. Such journals were identified using the impact and cite factor. The journals classified and ranked in A, B, and C categories were considered as credible journals and were used for content collection. Furthermore, where the Clarivate Analytics classification was not applicable, journal impact and cite factor was used to identify credible journals from which the articles and records were extracted. Tables 1, 2 present the Boolean words and the inclusion and exclusion criteria, respectively.
Table 1
Table 1. Search boundaries and keywords.
Table 2
Table 2. Inclusion and exclusion criteria.
2.1.3 Step 3: Study selection and evaluation
In this section, papers are identified and then screened based on the inclusion and exclusion criteria. Given a high volume of papers identified in the first stage, the first exclusion focused on the dripline as indicated in the databases. All papers whose topics refer to engineering are excluded. Papers are further scrutinized alongside the aim of the study. The screening is done using the title, abstract key and words. After the final review and screening, 53 papers were included in the dataset.
2.1.4 Step 4: Analysis and synthesis
The final step of the analysis summarizes the papers/documents based on the content, type of the study and field of the research. Several steps were followed in analyzing full text articles or records following the steps of Siva et al. (2016), we first established the categories: year, publication, type of articles and level of assessment, as shown in Table 3. Thematic analysis includes the frameworks, and their respective measurements used in different papers analyzed in this study.
Table 3
Table 3. Analytical categories.
2.1.5 Step 5: Reporting
The final step involves reporting the findings of the study and identifying the key research gaps that exist in the literature. Figure 2 summarizes the data collection and screening process of this study.
Figure 2
Figure 2. The flow chart for studies selection.
3 Findings
In this study, 58 documents were synthesized (see Table 4: summary of synthesized documents) and the findings are presented in tables and figure for easy interpretations.
Table 4
Table 4. List of documents included in the synthesis.
3.1 Yearly analysis
The annual numerical analysis of the 54 articles included in the dataset is shown in Figure 3. From the analysis, results show that most of the papers (42) included in the analysis were published in 2024. A significant number of the papers (10) were published in 2023. The remaining two paper were published in 2022 and 2021 with each year having one paper.
Figure 3
Figure 3. Yearly analysis.
The surge in publications in artificial intelligence especially in higher institution of learning is because many institutions of learning and research institutions consider artificial intelligence as a critical area that can drive improvement in learning processes.
3.2 Data sources
Several journals have published articles on artificial intelligence in higher institutions of learning as shown in Table 5. The selected 58 articles (records) on artificial intelligence in higher institutions of learning were published in 36 different journals. Computers and Education: Artificial Intelligence published the highest number (12) of articles followed by Heliyon and Procedia Computer Science that published 6 and 4 articles, respectively. Several other journals including Technology in Society, Technological Forecasting and Social Change, System, SCIENCE International Journal, Procedia, Patterns, Nurse Education Today, Learning and Individual Differences, Journal of Surgical Education, Journal of Open Innovation: Technology, Market, and Complexity published at least one journal article.
Table 5
Table 5. List of journals.
It can be noted that Computers and Education: Artificial Intelligence, Heliyon and Procedia Computer Science are the core journals publishing work on artificial intelligence in higher institutions of learning.
3.3 Regional coverage (analysis by country)
Table 6 and Figure 4 respectively present information about country where the research was undertaken. The findings show that most of the papers (10 out of 58) included in this synthesis came from USA while 4 papers came from Canada. The dominance of USA in publishing research on Artificial Intelligence in higher institutions of learning can be attributed to several factors. Firstly, the USA promotes open access to academic research and encourages dissemination of knowledge such that many institutions have open publication polices which increase visibility of research worldwide (Kankam et al., 2024). Secondly, the early adoption and innovation in education technology could be another factor explaining the dominance of the USA in publishing in Artificial Intelligence in higher institutions of learning. USA has a very long history of integrating technology into education ranging from e-learning platforms to Artificial intelligence-driven adaptive learning systems (Kabudi et al., 2021). Furthermore, many of these technologies originate from the US, and this gives researchers an early lead in studying and publishing in artificial intelligence in higher institutions of learning (Zawacki-Richter and Latchem, 2018).
Table 6
Table 6. Analysis according to region.
Figure 4
Figure 4. Continental analysis.
In terms of the continents, most of the documents analyzed came from Asia (14) followed by Europe with 10 documents. This is because these regions have invested a lot of resources in research and development. For instance, countries like China aiming to the worlds in Artificial Intelligence by 2030 has a national strategy with significant funding dedicated to Artificial Intelligence development (Roberts et al., 2021).
3.4 Artificial intelligence tools in higher institutions of learning
Artificial intelligence has been found to play a critical role in motivating students, raising their engagement levels and learning interest as well as academic performance (Owan et al., 2023; Nazari et al., 2021). Table 7 presents different categories of artificial intelligence tools commonly used in higher institutions of learning in education sector. From the findings, we note that most of the artificial intelligence tools reported in literature are the AI-Driven Research Tools. They include ChatGPT, Avide note, Elicit, Perplexity, Consensus, Semantic Scholar, Research Rabbit, Scholarcy, Mendeley, Zoterox, ChatPDF among others. Some of these tools are used as reference and data management tools while others are used to aid and improve writing. Commonly, Grammarly and writelab are used to improve on the grammar and the sentence formatting of the work.
Table 7
Table 7. Tools used in higher institutions of learning.
Artificial tools commonly used for teaching and conferencing include Zoom, Google Meet, Webex, Microsoft Teams, WhatsApp, Instagram and Moodle.
3.5 The impact of AI on stakeholders in institutions of higher learning
It is evident that artificial intelligence impacts several educational activities (Akinwalere and Ivanov, 2022; Southworth et al., 2023). This study sought to identify existing case studies where artificial intelligence has impacted different stakeholders (students, researchers, administrators tutors/lecturers) within higher institutions of learning (Slimi, 2023). The Table 8 shows different case studies indicating how artificial intelligence influences different stakeholders in higher institutions of learning.
Table 8
Table 8. Case studies showing the impact of AI on stakeholders in higher institutions of learning.
Chatbot technology positively impacts students’ learning and satisfaction. Chatbot is used as a powerful tool to teach entrepreneurship education programs in higher education. According to Vanichvasin (2022), Chatbot improve on students learning and satisfaction (Vanichvasin, 2022). Another AI too that has impacted education in higher education institutions is the Google meet technology. Eduwem et al. (2023) noted that Google meet technology helps in the generation of new information and knowledge. Google meet classroom is very useful and effective in improving students’ skills, abilities, discipline, and independent learning through teaching materials (Eduwem et al., 2023).
ChatGPT is a powerful research tool because it enhances information retrieval, data analysis, and idea generation while supporting drafting, editing, and summarization of texts (Huang and Tan, 2023). It provides methodological guidance, citation assistance, and access to multidisciplinary knowledge, making it useful for diverse research fields (Bettayeb et al., 2024). It allows scientists to focus on analyzing and interpreting literature reviews. By automating repetitive tasks and improving efficiency, ChatGPT helps researchers focus on critical thinking and analysis (Bettayeb et al., 2024). Embracing ChatGPT helps scientists produce meaningful research in a more efficient and effective manner (Bettayeb et al., 2024).
3.6 Opportunities for artificial intelligence in higher institutions of learning
Our literature synthesis shows that Artificial Intelligence presents several opportunities for enhanced learning, transformed teaching and administration as well as research (Akinwalere and Ivanov, 2022). Table 9 presents key areas in higher institutions of learning where Artificial Intelligence is critically needed.
Table 9
Table 9. Opportunities for AI in higher institutions of learning.
Artificial Intelligence tools are applied in almost all stages of research. Artificial Intelligence tools help in locating studies from different databases, analyze such studies and report findings. In terms of data analysis and interpretation, Artificial Intelligence tools can process and analyze vast amount of data quickly thus helping researchers to gain insights from complex datasets (Haleem et al., 2022). El Koshiry et al. (2023) and Akinwalere and Ivanov (2022) note that Artificial Intelligence tools improve the inclusion and accessibility of information for all. They also improve administrative tasks through automation.
3.7 Challenges of artificial intelligence use in higher institutions of learning
Technology plays a key role in equipping students with the necessary information and skills. With the information communication technology skills, students can achieve quality education free from the constraints of location and time (Akinwalere and Ivanov, 2022). Despite this promise the use of artificial intelligence tools (technologies comes with serious concerns and challenges). Table 10 below summarizes some of the challenges identified from the literature synthesis.
Table 10
Table 10. AI- related challenges.
Researchers report that over reliance on Artificial Intelligence affects the level of critical thinking (cognitive abilities) of the users. Zhai et al. (2024) noted that integrating AI dialogue systems in different educational subjects such as writing has a dual impact on leaners’ cognitive abilities. They noted that while these technologies enhance writing proficiency, boost self-confidence as well as streamline research tasks, they also introduce some risks including diminished creativity, plagiarism and bias (Zhai et al., 2024).
4 Discussion
The purpose of this review paper was to identify the Artificial Intelligence tools commonly used in higher institutions of learning, review the opportunities and challenges presented by the dominance of AI in higher institutions of learning. The discussion section focusses on the opportunities and challenges presented by Artificial Intelligence in higher institutions of learning.
4.1 Opportunities of artificial intelligence in higher institutions of learning
Ordinarily, researchers face challenges when it comes to conducting research the traditional way without the help of the Artificial Intelligence tools. By leveraging the computer’s cognitive power, researchers can conduct research with a lot of ease. Several tools including Mendeley, end note and Zoterox are useful for bibliometric and referencing of different materials cited in the work. With the Artificial Intelligence tools, the time spent conducting research is reduced. Artificial Intelligence- powered tools such as Research Rabbit, Sematic Scholarcy automate the literature review process, summarize main findings, identify methodologies and highlight research trends (Fabiano et al., 2024). Fabio et al. further noted that Artificial Intelligence tools and capabilities act as the cornerstone for modern automation of systematic reviews due to their large-language models (Fabiano et al., 2024). Tools such as OpenAI’s gpt3 and gpt4 are models specifically trained on very large datasets of text and able to demonstrate comprehension of such texts (Fabiano et al., 2024). With these capabilities, Artificial Intelligence enhances research and makes it less cumbersome to the researcher.
Through Artificial Intelligence-powered platforms, educational content can be tailored to suit individual learning styles, pace and capabilities (Bhutoria, 2022). With the aid of Artificial Intelligence technologies, students with learning disabilities, adaptive learning systems adjust lessons in real time to suite such students (Bhutoria, 2022). Furthermore, Artificial Intelligence-enabled personalized learning allows multimodal learning experiences using text, audio, video and other interactive elements. This allows learners to access information anywhere at any time (U.S. Department of Education, 2024). The multimodal approach allows all different learning needs and preferences to be met. Artificial Intelligence-tools help make global classrooms available to all including those speaking different languages through translator that creates power points subtitles in real time for what the instructor is saying (Akinwalere and Ivanov, 2022). The students who may not be able to attend class for one reason or the other are catered for since Artificial Intelligence-powered learning platforms are capable of breaking the silos between class and traditional ways of learning (Akinwalere and Ivanov, 2022).
The use of Artificial Intelligence tools to automate administrative tasks in higher institutions of learning is one key area where service has improved greatly (Osman et al., 2024). Queuing in lines to access a service given by the administrators should be something of the past. Students should be able to access services such as registration, verification and semester enrolment online using the artificial intelligence tools (Zhang et al., 2023). AI tools are found to refine and streamline the administrative tasks in different units in higher institutions of learning (Buetow and Lovatt, 2024).
4.2 Challenges/risks associated with the use of artificial intelligence tools in higher institutions of learning
Whereas the use of Artificial Intelligence tools in higher institutions has its own advantages as already discussed, there are also some key concerns that need to be addressed. The cases of academic dishonesty including fabrication of data using Artificial Intelligence tools is on the rise (Chen et al., 2024). The proliferation of Artificial Intelligence tools has led to falsification, fabrication of data, plagiarism as well posing dilemma in maintenance of ethical standards in research (Chen et al., 2024). A lot of cases of misconduct facilitated by the use of sophisticated Artificial Intelligence tools have spotlighted the vulnerabilities especially in regulatory systems and this calls for vigilance while indulging heavily in the use of Artificial Intelligence tools (Chen et al., 2024). Complaints of lack of necessary transparency while using Artificial Intelligence technology in research have also been reported. In data processing and results generation, algorithms are used. Researchers may not know the working principles and the processes of decision making undertaken by in the algorithms. As a results, wrong interpretations can be attached to the results generated (Chen et al., 2024).
Overreliance on Artificial Intelligence tools especially for problem solving and generating content creates an environment for passive learning which is counterproductive when it comes to developing learners who are critical thinkers (Darwin et al., 2024). Therefore, the utilization of Artificial Intelligence tools in institutions of learning more so by the students and instructors should be approached with care so that the intended purpose of enhancing critical thinking as premised as one of the benefits should be bolstered rather than diminished.
5 Conclusions and recommendation
The purpose of this study was to identify the opportunities for Artificial Intelligence in education specifically, identify the opportunities and challenges. The review unveiled many opportunities including enhanced research capabilities, automation of administrative tasks among others. Artificial Intelligence tools are found to refine and streamline the administrative tasks in different units in higher institutions of learning. The challenges include ethical concerns, integrity issues and data fabrication issues.
Despite the concerns raised in literature, the benefits of Artificial Intelligence cannot be over emphasized. Artificial intelligence remains a powerful tool for research, automation of administrative tasked, personalized learning, inclusivity and accessibility of educational content for all. Emphasis should be put in regulatory frameworks detailing how such tools can be used while maintaining the level of ethical standards required. Furthermore, whereas, there is a significant progress in leveraging artificial intelligence to enhance educational tasks such as administrative tasks, including summarizing existing research and records management, there is limited progress in meeting specific requirements of educators especially in assessment of the education outcomes for the learners. Consequently, AI tools have yet to fully align with the specific requirements of educators.
Author contributions
SO: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing. JE: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. SA: Investigation, Project administration, Supervision, Writing – review & editing. CO: Data curation, Formal analysis, Methodology, Visualization, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
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Keywords: artificial, intelligence, innovations, opportunities, challenges, literature
Citation: Ocen S, Elasu J, Aarakit SM and Olupot C (2025) Artificial intelligence in higher education institutions: review of innovations, opportunities and challenges. Front. Educ. 10:1530247. doi: 10.3389/feduc.2025.1530247
Edited by:
Chayanika Uniyal, University of Delhi, IndiaReviewed by:
Toolika Wadhwa, University of Delhi, IndiaRajiv Kumar Verma, University of Delhi, India
Copyright © 2025 Ocen, Elasu, Aarakit and Olupot. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Samuel Ocen, b2NlbnMuc2FtdWVsQGdtYWlsLmNvbQ==