Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications

Research examining the impact of AI on higher education has witnessed substantial growth in recent years, as highlighted by notable studies (Al-Zahrani, 2023; Al-Zahrani, 2024a; Bozkurt et al. 2021; Chu et al. 2022; Dai and Ke, 2022; Laupichler et al. 2022; Zawacki-Richter et al. 2019). Scholars from diverse fields, including education, computer science, psychology, and ethics, have explored various facets of AI implementation in higher education settings. Chu et al. (2022) scrutinized the top 50 AI studies in higher education from the Web of Science (WoS) database. Their analysis revealed a prevalent focus on predicting learners’ learning status, particularly dropout and retention rates, student models, and academic achievement. However, there is a noticeable lack of emphasis on higher-order thinking skills, collaboration, communication, self-efficacy, and AI skills in higher education studies (Chu et al. 2022). Laupichler et al. (2022) stress that research on AI in higher education is still in its early stages, necessitating refinement in defining AI literacy and determining appropriate content for non-experts to enhance their understanding of AI. This literature review provides an overview of key research areas and offers insights into existing knowledge.

Pedagogical innovations

One pivotal research domain explores the pedagogical implications of AI in higher education, recognizing its potential to revolutionize the educational process and enhance efficiency (Al-Zahrani, 2023; Al-Zahrani, 2024a; Kuleto et al. 2021; Zheng et al. 2023). AI integration in transnational higher education, including distance and online education, holds the promise of improving efficiencies and transforming management, administration, student recruitment, and pedagogical processes, leading to enhanced sustainability and development (El-Ansari, 2021). Huang (2018) emphasizes AI’s role in innovating education, noting its ability to transform learning interactions from machine-focused to knowledge-centered approaches based on learner needs.

Numerous studies delve into how AI-powered technologies, such as intelligent tutoring systems and adaptive learning platforms, enhance personalized learning experiences, promote student engagement, and improve academic outcomes (Al-Zahrani, 2023; Al-Zahrani, 2024a; Chu et al. 2022; Dai and Ke, 2022). Kuleto et al.‘s (2021) findings demonstrate the significance of AI in improving learning outcomes, particularly in enhancing students’ skills, promoting collaborative learning, and providing a more accessible research environment. Additionally, Seo et al. (2021) highlight the potential of incorporating AI systems in online learning to facilitate personalized learner-instructor interactions on a large scale. Kochmar et al. (2022) present experimental results showing that AI tutoring systems lead to significant overall improvements in student performance outcomes.

Furthermore, AI has the potential to transform higher education by enhancing teaching and learning, improving assessment and feedback, increasing access and retention, reducing costs and time, and supporting administration and management (Abdous, 2023; Al-Zahrani, 2024a; Bates et al. 2020; Chu et al. 2022; Popenici and Kerr, 2017; UNESCO, 2021). Almaiah et al.‘s (2022) study suggests a positive inclination towards integrating AI into educational environments, attributing it to AI being recognized as an innovative teaching tool. Huang (2018) observes positive effects of AI teaching systems on environmental education for college students.

Moreover, AI can revolutionize social interactions within higher educational settings, impacting learners, teachers, and technological systems (Al-Zahrani, 2023; Al-Zahrani, 2024a; Dai and Ke, 2022). Crown et al. (2011) demonstrate the positive impact of an interactive chatbot on engineering students’ engagement and motivation. Essel et al. (2022) find that students engaging with a virtual teaching assistant (chatbot) show improved academic performance. Kumar (2021) observes the positive impacts of chatbots on learning performance and teamwork.

Learning analytics and student support

AI’s potential for innovation in education is prominent in the realm of learning analytics and student support. There has been a shift towards utilizing student data and analytics to enhance the educational experience and improve learning outcomes (André Renz, 2021; Huang et al. 2021; Zheng et al. 2023). AI technologies enable real-time analysis of vast amounts of data not limited to students’ learning but about their emotions as well, offering advantages in identifying at-risk students, recommending personalized interventions, and facilitating timely feedback and assessment (Zhi Liu et al. 2024; X. Liu et al. 2023). Learning analytics and AI-driven student support systems can provide actionable insights to educators and enhance student success (André Renz, 2021; MET, 2022; Ouyang et al. 2023; Gallagher, 2020).

For example, Dong and Hu’s (2019) study successfully identified contextual factors differentiating high-achieving and low-achieving students in reading literacy using machine learning techniques. Li et al.‘s (2022) optimized AI-based genetic algorithm grouping method for collaborative groups in higher education outperformed traditional grouping methods. Ouyang et al. (2023) utilized AI algorithms and learning analytics to analyze groups’ collaboration patterns in online interaction settings.

Assessment and grading

AI’s role in automating assessment and grading processes is another significant area of interest. Scholars investigate the reliability and validity of AI-based grading systems, comparing them to traditional human grading methods, and explore the potential benefits and limitations of automated grading (Lockwood, 2014; CTL, 2023; CUPA, 2023; McNulty, 2023; Chen, 2023). AI in assessment, including Natural Language Processing (NLP) and plagiarism detection, can automate grading, reduce workload, and enable data-driven decision-making (Lockwood, 2014; CTL, 2023; CUPA, 2023; McNulty, 2023; Chen, 2023).

While the potential benefits of AI in assessment and grading are significant, it’s also important to consider its practical applications and impact on student outcomes. For instance, Susilawati et al.‘s (2022) study explored the positive influence of digital assessment and trust on student character and academic performance. Additionally, Hooda et al.‘s (2022) examination of AI-driven assessment and feedback practices revealed positive impacts on students’ learning outcomes. Learning analytics, in this context, enables higher education institutions to support the learning environment at multiple levels.

Educators’ professional development

AI contributes to pedagogical innovation in the domain of educators’ professional development. Research explores the implications of AI for educators’ professional development, focusing on how AI technologies can support instructors in developing positive perceptions and attitudes, adaptive teaching strategies, and personalized learning experiences (Al-Zahrani, 2023; Al-Zahrani, 2024a; CTL, 2023; Chen, 2023; Seo et al. 2021).

As AI integration in education progresses, it becomes increasingly important to address the concerns and training needs of educators. Educational institutions, policymakers, and AI developers must collaboratively address concerns regarding AI integration and provide the necessary support and training for educators to effectively implement AI technologies in their teaching practices (Al-Zahrani, 2024a).

One example of how AI can be applied to enhance educators’ professional development is through the use of machine learning to analyze student feedback. Esparza et al.‘s (2018) ‘SocialMining’ model utilizes machine learning algorithms to enhance teaching techniques based on student comments on teacher performance. Integrating AI into educators’ professional development holds promise for improving instructional practices and the overall quality of education, providing targeted support and personalized learning resources.

Ethical and social implications

The ethical and social dimensions of AI in higher education are critical considerations. AI’s advancement introduces ethical challenges and concerns, necessitating further research to explore the social implications of AI, including accountability in AI-mediated practices and its influence on teaching and learning relationships (Al-Zahrani, 2024b; Bearman et al. 2022). Challenges related to privacy, ethics, and morality in AI-driven approaches require interdisciplinary collaborations for comprehensive research and development (Al-Zahrani, 2024b; Hu et al. 2023; Zhang and Aslan, 2021).

Scholars delve into issues of algorithmic bias, discrimination, fairness, transparency, and accountability in AI-driven educational systems (Al-Zahrani, 2024b; UNESCO, 2021; Abdous, 2023; Schiff, 2022). Ethical considerations in deploying AI technologies, ensuring equity and inclusivity, and balancing human instructors’ roles with AI tools are explored. The societal impact of AI, including changes in employment patterns and the transformation of the workforce, requires careful consideration (Bates et al. 2020; Popenici and Kerr, 2017; Lo Piano, 2020; Seo et al. 2021; Chen, 2023).

Ethical considerations in integrating AI into everyday environments should be thoroughly addressed (Al-Zahrani, 2024b; Doncieux et al. 2022). This includes examining AI’s impact on human life and societies. Dignum (2017) emphasizes the importance of upholding societal values, considering moral and ethical implications, and ensuring transparency in AI reasoning processes. In Seo et al.‘s (2021) study, concerns arise regarding responsibility, agency, surveillance, and potential privacy violations by AI systems. Raising awareness about human-centered values and responsible, ethical AI development is crucial in addressing these concerns (Al-Zahrani, 2024b; André Renz, 2021).

Table 1 summarizes the existing focus areas within each research domain related to AI in higher education and highlights the identified gaps that warrant further investigation. These gaps include aspects such as higher-order thinking skills, collaboration and communication, development of AI skills, large-scale implementation of learning analytics, ethical considerations in assessment and grading, addressing educators’ concerns and training needs, and a comprehensive exploration of ethical concerns and societal impacts.