A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour

The evidence syntheses that addressed a variety of AI applications or AI more generally (n = 31; see Additional file 5: Appendix E) were also coded inductively for benefits and challenges. Only two reviews considered AIHEd affordances (Crompton & Burke, 2023; Rangel-de Lázaro & Duart, 2023), four did not mention any benefits, and six reviews did not mention any challenges, which for four reviews were due to their bibliometric nature (Gudyanga, 2023; Hinojo-Lucena et al. 2019; Maphosa & Maphosa, 2021; Ullrich et al., 2022).

Benefits of using AI in higher education

Twelve benefits were identified across the 31 reviews (see Additional file 12: Appendix L), with personalised learning the most prominent (see Table 6). A 32.3% share of reviews identified greater insight into student understanding, positive influence on learning outcomes, and reduced planning and administration time for teachers. The top six benefits will be discussed below.

Zawacki-Richter et al. (2019) and Sourani (2019) noted the adaptability of AI to create personalised learning environments, enabling the customisation of educational materials to fit individual learning needs (Algabri et al., 2021; Buchanan et al., 2021), and thereby support student autonomy by allowing learning at an individual pace (Alotaibi, 2023; Bearman et al., 2023). Diagnostic and remedial support is another focus, particularly in tailoring learning paths based on knowledge structures, which can facilitate early interventions for potentially disengaged students (Alam & Mohanty, 2022; Chu et al., 2022). Interestingly, ten reviews found or mentioned the ability of AI to positively influence learning outcomes (e.g., Alotaibi & Alshehri, 2023; Fichten et al., 2021), yet few reviews in this corpus provided real evidence of impact (as mentioned above in Assessment and Evaluation). AI was identified, however, as enhancing learning capabilities and facilitating smoother transitions into professional roles, especially in nursing and medicine (Buchanan et al., 2021; Hwang et al., 2022; Sapci & Sapci, 2020), alongside stimulating student engagement (Chaka, 2023) and honing specific skills such as writing performance through immediate feedback systems (Ouyang et al., 2020). Several reviews highlighted that AI could automate routine tasks and thereby reduce planning and administrative tasks (e.g., Alam & Mohanty, 2022). For instance, AI-powered chatbots and intelligent systems facilitate lesson planning and handle student inquiries, which streamlines the administrative workflow (Algabri et al., 2021), and automated grading systems can alleviate workload by assessing student performance (e.g., Crompton & Burke, 2023).

Several reviews highlighted the role of machine learning and analytics in enhancing our understanding of student behaviours to support learning (e.g., Alotaibi & Alshehri, 2023) and, complementing this, Ouyang et al. (2020), Rangel-de Lázaro and Duart (2023), and Salas-Pilco and Yang (2022) found primary research that focused on the utility of predictive systems. These systems are designed for the early identification of learning issues among students and offer guidance for their academic success. Reviews identified studies analysing student interaction and providing adaptive feedback (e.g., Manhiça et al., 2022), which was complemented by Alam and Mohanty (2022), who highlighted the role of machine learning in classifying patterns and modelling student profiles. Predictive analytics is further supported by reviews such as Salas-Pilco et al. (2022) and Ouyang et al. (2020), which discuss their utility in enabling timely interventions.

Seven reviews noted the potential of AI to advance equity in education, with universities’ evolving role in community development contributing to this (Alotaibi & Alshehri, 2023). In the future, AI could provide cheaper, more engaging, and more accessible learning opportunities (Alam & Mohanty, 2022; Algabri et al., 2021), such as using expert systems to assist students who lack human advisors (Bearman et al., 2023), thereby alleviating social isolation in distance education (Chaka, 2023). In India, AI has also been discussed with regards to innovations such as the ‘Smart Cane’ (Bhattacharjee, 2019). AI’s potential to enrich and diversify the educational experience (Manhiça et al., 2022), including alleviating academic stress for students with disabilities (Fichten et al., 2021), was also discussed.

Algabri et al. (2021) describe how AI can not only improve grading but also make it objective and error-free, providing educators with analytics tools to monitor student progress. Ouyang et al. (2020) note that automated essay evaluation systems improve student writing by providing immediate feedback. Zhang et al. (2023) found that machine learning could reveal objective skills indicators and Kirubarajan et al. (2022) found that AI-based assessments demonstrated high levels of accuracy. However, other studies discuss the relevance of AI in healthcare, providing tools for data-driven decision making and individualised feedback (Charow et al., 2021; Saghiri et al., 2022). Collectively, these studies indicate that AI holds promise for making educational assessments more precise, timely, and tailored to individual needs.

Challenges of using AI in higher education

The 31 reviews found 17 challenges, but these were mentioned in fewer studies than the benefits (see Additional file 12: Appendix L). Nine studies (see Table 7) reported a lack of ethical consideration, followed by curriculum development, infrastructure, lack of teacher technical knowledge, and shifting authority, which were identified in 22.6% of studies. Reviews discuss the ethical challenges that medical professionals face when interpreting AI predictions (Grunhut et al., 2021; Lee et al., 2021). AI applications in education also raise ethical considerations, ranging from professional readiness to lapses in rigour, such as not adhering to ethical procedures when collecting data (e.g., Salas-Pilco & Yang, 2022), and ethical and legal issues related to using tools prematurely (Zhang et al., 2023). Chu et al. (2022) explored the ethical challenges in balancing human and machine-assisted learning, suggesting that educators need to consciously reflect on these issues when incorporating AI into their teaching methods.

In relation to the challenges of integrating AI into education, curriculum development issues and infrastructural problems span from broad systemic concerns to specific educational contexts. According to Ouyang et al. (2020), there is a disconnect between AI technology and existing educational systems, and suggest the need for more unified, standardised frameworks that incorporate ethical principles and advocate for the development of multidisciplinary teams (Charow et al., Lee et al., 2021), with a stronger focus on more robust and ethically aware AI curricula (e.g., Grunhut et al., 2021). Furthermore, despite its potential, a country may lag behind in both AI research and digital infrastructure (Bhattacharjee, 2019) with technical, financial and literacy barriers (Alotaibi & Alshehri, 2023; Charow et al., 2021), such as the high costs associated with developing virtual programming and high-speed internet (Harmon et al., 2021).

With the potential to slow AI curriculum development and application efforts, several reviews mentioned a lack of teacher technical knowledge, reporting that many educators would need new skills in order to effectively use AI (Alotaibi & Alshehri, 2023; Bhattacharjee, 2019; Chu et al., 2022; Grunhut et al., 2021; Lee et al., 2021). While it was reported that faculty generally lack sufficient time to integrate AI effectively into the curriculum (Charow et al., 2021), this was compounded by the fear of being replaced by AI (Alotaibi & Alshehri, 2023; Bearman et al., 2023). To this end, Charow et al. (2021) emphasise the need to see AI as augmenting rather than replacing. At the same time, it has been recognised that a lack of AI literacy could lead to a shift in authority moving decision-making from clinicians to AI systems (Lee et al., 2021). Overcoming resistance to change and solving various challenges, including those of an ethical and administrative nature, was identified as pivotal for successful AIHEd integration (Sourani, 2019).