The Artificial Intelligence (AI) Landscape in Higher Education (HE): Current Developments, Opportunities, and Threats
The Evolution of Artificial Intelligence in Higher Education
ISBN: 978-1-83549-487-5, eISBN: 978-1-83549-486-8
Publication date: 25 November 2024
Abstract
The evolution of Artificial Intelligence (AI) in higher education marks a paradigm shift, driving significant changes in pedagogical approaches and learning methodologies. With the rise of generative AI and artificial general intelligence (AGI), institutions have witnessed a transformative era where traditional content creation and delivery are being redefined. Start-ups like OpenAI and Anthropic have been at the forefront, offering tools like ChatGPT and Claude-3, which reshape natural language processing and forecast a future where AI integrations are seamless and pervasive. This chapter provides a critical overview of the current AI-driven applications enhancing personalized learning, content generation, and remote learning. Tools such as Mainstay, CourseGenie, and AIDES demonstrate AI's capacity to improve student engagement and success rates, while Degreed and Gnowbe showcase the broadening horizons of AI in skills building and microlearning experiences. Furthermore, platforms like Elicit and Research Rabbit exemplify the transformation in research and academic writing, albeit not without raising ethical concerns. In conclusion, AI's permanence in the educational landscape is unquestionable, calling for strategic frameworks that empower educators and students to harness its benefits effectively. The imminent expansion of the AI tool ecosystem necessitates preparedness for substantial shifts in educational practices, where ethical considerations and value-based strategies become paramount. Higher education institutions must align with this technological momentum, ensuring AI's potential is maximized in an ethical, inclusive, and impactful manner.
Keywords
Citation
Lytras, M.D., Alkhaldi, A., Malik, S., Serban, A.C. and Aldosemani, T. (2024), "The Artificial Intelligence (AI) Landscape in Higher Education (HE): Current Developments, Opportunities, and Threats", Lytras, M.D., Alkhaldi, A., Malik, S., Serban, A.C. and Aldosemani, T. (Ed.) The Evolution of Artificial Intelligence in Higher Education (Emerald Studies in Active and Transformative Learning in Higher Education), Emerald Publishing Limited, Leeds, pp. 1-10. https://doi.org/10.1108/978-1-83549-486-820241001
Publisher
:Emerald Publishing Limited
Copyright © 2025 Miltiadis D. Lytras, Afnan Alkhaldi, Sawsan Malik, Andreea Claudia Serban and Tahani Aldosemani. Published under exclusive licence by Emerald Publishing Limited
Introduction: the Evolution of AI in Higher Education (HE)
The use of technologies for the advancement of higher education has been witnessed in the last years with diverse waves of technology utilization including social media, learning management systems, open source technologies, virtual reality, learning analytics, cloud computing (Alsaywid et al., 2023; Lytras et al., 2016; Arafat et al., 2019; Lytras & Mathkour, 2017; Lytras et al., 2018; Daud et al., 2017; Zhang et al., 2016; Zhang et al., 2018; Sicilia et al., 2006). In recent years, the landscape of HE has been dynamically altered by the advent of generative AI and artificial general intelligence (AGI) (Lytras & Visvizi, 2021; Lytras, 2023). The last two years, in particular, have witnessed a strong wave of sustainable AI services entering the market, initiating a new era in the landscape of the application of educational technology in academia. These innovations signal not merely a trend but a fundamental shift in how educational content is created, consumed, and delivered. It is about an AI revolution that affects every aspect of HE (Table 1.1, Fig. 1.1).
Among the forerunners of this AI revolution in general are companies like OpenAI with ChatGPT, Stability AI, Infection AI, Hugging face and Anthropic. These entities represent a fraction of the evolving rapidly expanding AI ecosystem, yet their contributions have been pivotal. Each start-up brings a unique flavor to the table – OpenAI's ChatGPT has revolutionized natural language processing, while Anthropic's Claude-3 offers a promising glimpse into the future of AGI, Infection AI brings the first emotional intelligence AI model the PI.ai.
The implications of these AI models are vast and varied, extending into diverse educational services in HE including personalized learning, content generation, and remote learning enhancement, alongside Automated Assessment and Adaptive Testing. Each domain offers a gateway to untapped potential, optimizing resources and forecasting student success through data-driven research and advanced machine learning models.
However, the utilization and exploitation of the AI-enabled educational tools and services force HE institutions to adopt a robust AI strategy to be applied end to end to the entire organization. Such a strategy should not only leverage these technological advancements but also align them with the core educational mission of fostering inclusive and transformative learning experiences. It's imperative that institutions prepare for a future where AI is not a supplementary tool but a foundational pillar in the architecture of HE.
Thus, the pursuit of integrating AI in HE is more than an educational endeavor; it is a strategic imperative that demands attention, resources, and a forward-looking vision. The aim is to equip institutions with the capability to harness AI for creating a more adaptable, personalized, and effective learning environment – one that is primed for the challenges and opportunities of the digital age, capable of delivering a positive social, economic, and cultural impact.
AGI and Its Impact on HE
Artificial Intelligence (AI)
The evolution of AI, as described in the previous section, has contributed to a variety of very interesting applications and services that provide value to students, faculty, and other stakeholders. The ecosystem of these generative AIs is briefly summarized in Fig. 1.1.
In Table 1.1, we summarize few of them, and also, we comment on their unique value proposition. The first application commented is Mainstay, which shared vision according to its founders is “to make college access more equitable through technology”. Their initial start-up AdmitHub, a student engagement platform that uses behaviorally intelligent chatbots to connect students with the support they need to reach college led to the Mainstay which took further this objective. It is about helping students to support all their workflows in meeting their goals for a successful career. Their platform, https://mainstay.com/platform/, offers AI-enabled engagement scenarios with each student based on AI chatbots offering unique support to all learners. According to the company, their platform promotes and improves student engagement and enhances the success rates of students.
Examples of AI-Enabled Educational Services.
| Title | Company | Indicative Services |
|---|---|---|
| MainStay | https://mainstay.com/about/ |
|
| AIDES | https://www.eduaide.ai/ |
|
| CourseGenie | https://www.coursegenie.ai/ |
|
| Degreed | https://www.degreed.com/ |
|
| Gnowbe | https://www.gnowbe.com/ |
|
CourseGenie, https://coursegenie.ai/, is also targeting the same area. It provides sophisticated AI-enabled services for learning content authoring and creation, offering multifaceted tools to personalized and transformative learning. With integrative workflows, instructors can develop dynamic courses by exploiting applications' options to generate automatically any of the following information (course description, outline, lesson plans, engaging contexts, and other). This is a straightforward direction for this market segment of AI for education.
Another interesting example of real-world AI-enabled services is AIDES, or AI-assisted education, https://www.eduaide.ai/. This company offers a robust teacher's assistant tool, personalization tools, feedback Bot, Eduaide chat, and many other interesting value adding services. It offers sophisticated courses and learning resources recommendation and also study planning support. It also provides a very comprehensive and integrated learning object authoring tool.
Degreed (https://Degreed.com) is another company that integrated learning, personalization, and skills building. According to Degreed's statement available on their website, the service leads individuals and companies to a skills-first future with end-to-end learning, targeted skill-building, and real-time skills data. They offer sophisticated tools for the development of the entire learning ecosystem (LXP), academies for unique and integrated learning experiences, and also sustainable content marketplaces.
GnowBe (https://www.gnowbe.com/) is about designing and creating interactive, group-based microlearning experiences. With emphasis on business teams, companies, and individuals, GnowBe offers unique opportunities for learning, development, and engagement. Onboarding, sales enablement, and learning and development are some of their indicative solutions.
List of AI platforms in the HE landscape with their corresponding URLs integrated into the descriptions:
Current Developments in the Field of AI in HE
In the same context recently, a new variety of AI-enabled tools for research and summarization of scientific knowledge have appeared. Several of them not only provide robust services that increase productivity and efficiency but also challenge ethical issues including academic integrity and plagiarism. For some academics, these tools should be blamed since they disrupt entirely the professional conduct in academia, and for some others, this is just the revolution that must be exploited on an ethical way for the common good.
In the next paragraphs, we selectively present few of them.
Elicit, https://elicit.com/, is a new AI-enabled service that enables the fast, efficient and trusted knowledge elicitation from million academic paper, supporting thousands of researchers worldwide. The service has the capacity to disrupt the long-standing and time-consuming human-led workflows for summarizing, exploiting scientific knowledge. It works really effectively with open access repositories of scientific papers and also allows uploading of PDF versions of selected literature.
Connected Paper, https://www.connectedpapers.com/, is another very effective tool for identified and exploiting relevant literature, offering unique ways for navigating to scientific content. With the deployment of similarity algorithms, it suggests connected papers and research based on users' preferences. The quality of outcomes is fair, and there is a lot of space for improvement. According to the applications' statement, it is a very good tool for getting a fast visual grasp of the trends within a scientific domain.
Research Rabbit, https://researchrabbitapp.com/home, contributes also in the same area of novel tools for fostering exploration of literature and scientific knowledge. With a user-friendly interface, users can exploit relevant literature and can really fast develop collections of focused literature. Value adding services including extraction of references and integration with citation management tools like Zotero also add significant value for researchers.
In Fig. 1.2, we provide an overview of the interface of Research Rabbit.
In another direction, tools like WordTune and Quillbot (shown in Fig. 1.3) offer unforeseen opportunities for professional and academic writing.
Wordtune, https://www.wordtune.com/, is an AI tool that allows users to enhance significantly academic texts by providing paraphrasing, improvements, and newly generated text in different tones. For sure, tools like these support students to develop really fast papers, and challenge the ethical stands of their work, but this is a very limited and myopic interpretation of the phenomenon. It is critical in HE to investigate and to reinvent the way of assessments focusing on critical thinking assessment and deployment of classical taxonomies of learning objectives with the utilization of new AI tools.
Carnegie Learning has been deeply involved in educational research and development, particularly in the field of problematics search theory. This theory, originating from the Carnegie School tradition, describes a process through which organizations learn from feedback to enhance performance.
Carnegie Perspectives cover various educational domains, including developmental mathematics and learning from emerging teaching practices, demonstrating the institution's multifaceted approach to education research and development. The foundation's focus on a “learning-by-doing” approach in improvement work in education emphasizes a practical and hands-on method to promote educational advancements.
Smart Sparrow is an award-winning adaptive learning platform, renowned for its broad applicability in undergraduate studies including chemistry, neuroscience and more. Its online interactive resources, like the Virtual Microscopy Adaptive Tutorials (VMATs), have revolutionized learning in complex subjects by tailoring experiences to individual student needs, improving engagement and performance. It also excels in merging theory with practice in virtual labs, essential for medical and pathology students, demonstrating its comprehensive impact on education. Querium: Provides AI-powered learning tools that help students prepare for standardized tests.
Querium is a collaborative search platform that enhances user experience by allowing search, sharing and team collaboration with session-based controls, personalized by search history. It combines AI with features like relevance feedback and faceted search to deliver tailored content and improve knowledge sharing in educational settings. As part of a network aimed at fostering algorithmically mediated collaboration, Querium sets itself apart with advanced collaborative tools and awareness features.
Duolingo is a user-friendly language learning app that offers a game-like experience to bolster language skills, equating to a semester of university instruction. It's proven to improve vocabulary and language proficiency, with its gamified approach and instant feedback fostering self-directed learning and motivation. While it excels as a supplementary tool, its integration into formal education is also beneficial.
Gemini, an AI chatbot by Google, uses advanced language processing to interact conversationally, excelling in creating patient education materials for neurological conditions. While its performance in Vietnamese biology exams was modest at 49.5%, it has been recognized for exceptional readability, albeit with lower appropriateness scores than human experts. Studies comparing Gemini with counterparts like ChatGPT reveal its strengths and limitations, highlighting its educational impact and potential for further development.
These AI tools offer a wide range of capabilities that can significantly assist both students and faculty in their educational pursuits, research, and professional development. Each tool brings innovative solutions to streamline processes, enhance productivity, and improve learning experiences.
Conclusions
AI is here to stay in the world of HE. As we've seen both its potential and challenges, it's clear that a solid plan is essential. This plan must equip teachers and students with the skills they need to make the most of AI. Along with this, we need investment in the right tools and a commitment to teach people how to use them effectively.
The next few years will likely see a big wave of new AI tools designed for learning. It's crucial for schools and universities to be prepared for big changes in how they teach and how students learn. By getting ready for these changes, institutions can make sure that they use AI in a way that's ethical and really benefits students. To do this well, we need clear guidelines and policies that make sure AI is used in a way that adds real value to education. With the right approach, AI can help us teach and learn better than ever before.
References
Alsaywid et al., 2023 Alsaywid, B. S. , Alajlan, S. A. , & Lytras . (2023). Transformative learning as a bold strategy for the vision 2030 in saudi arabia: Moving higher healthcare education forward. In Technology-enhanced healthcare education: Transformative learning for patient-centric health (pp. 187–207). https://doi.org/10.1108/978-1-83753-598-920231014
Arafat et al., 2019 Arafat, S. , Aljohani, N. , Abbasi, R. , Hussain, A. , & Lytras, M. (2019). Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study. Computers in Human Behavior, 92, 478–486. https://doi.org/10.1016/j.chb.2018.02.026
Daud et al., 2017 Daud, A. , Lytras , Aljohani, N. R. , Abbas, F. , Abbasi, R. A. , & Alowibdi, J. S. (2017). Predicting student performance using advanced learning analytics. Presented at the 26th International World Wide Web Conference 2017, WWW 2017 Companion. https://doi.org/10.1145/3041021.3054164
Lytras, 2023d Lytras, M. D. (2023). Forty definitions and metaphors for active and transormative learning in chat gpt times: Chat gpt as an active and transformative technology enhanced learning boost in healthcare education. In Technology-enhanced healthcare education: Transformative learning for patient-centric health (pp. 19–34). https://doi/org/10.1108/978-1-83753-598-920231002
Lytras et al., 2018 Lytras, M. D. , Aljohani, N. R. , Visvizi, A. , Ordonez De Pablos, P. , & Gasevic, D. (2018). Advanced decision-making in higher education: Learning analytics research and key performance indicators. Behaviour & Information Technology, 37(10–11), 937–940. https://doi.org/10.1080/0144929X.2018.1512940
Lytras et al., 2016 Lytras, M. D. , Damiani, E. , & Mathkour, H. (2016). Virtual reality in learning, collaboration and behaviour: Content, systems, strategies, context designs. Behaviour & Information Technology, 35(11), 877–878. https://doi.org/10.1080/0144929X.2016.1235815
Lytras and Mathkour, 2017 Lytras, M. D. , & Mathkour, H. (2017). Advances in research in social networking for open and distributed learning. International Review of Research in Open and Distance Learning, 18(1), i–iv. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014060851&partnerID=40&md5=eaed10b7da51187b3f6f64b3ced437a2
Lytras and Visvizi, 2021a Lytras, M. , & Visvizi, A. (2021). Artificial intelligence and cognitive computing: Methods, technologies, systems, applications and policy making. Sustainability, 13(7). https://doi.org/10.3390/su13073598
Sicilia et al., 2006 Sicilia, M.-A. , Lytras, M. , Rodríguez, E. , & García-Barriocanal, E. (2006). Integrating descriptions of knowledge management learning activities into large ontological structures: A case study. Data & Knowledge Engineering, 57(2), 111–121. https://doi.org/10.1016/j.datak.2005.04.001
Zhang et al., 2016 Zhang, X. , Chen, H. , de Pablos, P. O. , Lytras , & Sun, Y. (2016). Coordinated implicitly? An empirical study on the role of social media in collaborative learning. International Review of Research in Open and Distance Learning, 17(6), 121–144.
Zhang et al., 2018 Zhang, X. , Zhang, Y. , Sun, Y. , Lytras, M. , Ordonez de Pablos, P. , & He, W. (2018). Exploring the effect of transformational leadership on individual creativity in e-learning: A perspective of social exchange theory. Studies in Higher Education, 43(11), 1964–1978. https://doi.org/10.1080/03075079.2017.1296824
Bibliography
Alsaywid et al., 2021 Alsaywid, B. , Lytras , Abuzenada, M. , Lytra, H. , Sultan, L. , Badawoud, H. , Abuzenada, W. , Alhaider, S. A. , Housawi, A. , & Apostolaki, A. (2021). Effectiveness and Preparedness of Institutions' E-Learning Methods During the COVID-19 Pandemic for Residents' Medical Training in Saudi Arabia: A Pilot Study. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.707833
Chui et al., 2020 Chui, K. T. , Fung, D. C. L. , Lytras, M. D. , & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 107, 105584. https://doi.org/10.1016/j.chb.2018.06.032
Lytras, 2023b Lytras, M. D. (2023). Active and transformative learning in STEAM disciplines: From curriculum design to social impact. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175959508&partnerID=40&md5=7a0a70bf5200364fbb293e06a78e0a07
Lytras and Alsaywid, 2023 Lytras, M. D. , & Alsaywid, B. S. (2023). Next generation healthcare education and research: Utilizing the talent, skills, and competencies for value-based healthcare. In Active Learning for Digital Transformation in Healthcare Education, Training and Research (pp. 211–220). https://doi.org/10.1016/B978-0-443-15248-1.00005-9
Lytras and Housawi, 2023 Lytras, M. D. , & Housawi, A. (2023). Active learning in healthcare education, training, and research: A digital transformation primer. In Active learning for digital transformation in healthcare education, training and research (pp. 1–11). https://doi.org/10.1016/B978-0-443-15248-1.00006-0
Lytras et al., 2020 Lytras, M. , Sarirete, A. , & Damiani, E. (2020). Technology-enhanced learning research in higher education: A transformative education primer. Computers in Human Behavior, 109, 106350. https://doi.org/10.1016/j.chb.2020.106350
Lytras et al., 2022 Lytras, M. , Serban, A. C. , Ruiz, M. J. T. , Ntanos, S. , & Sarirete, A. (2022). Translating knowledge into innovation capability: An exploratory study investigating the perceptions on distance learning in higher education during the COVID-19 pandemic - The case of Mexico. Journal of Innovation and Knowledge, 7(4), 100258. https://doi.org/10.1016/j.jik.2022.100258
- Prelims
- Chapter 1 The Artificial Intelligence (AI) Landscape in Higher Education (HE): Current Developments, Opportunities, and Threats
- Chapter 2 Integrating Artificial Intelligence in Higher Education: Enhancing Interactive Learning Experiences and Student Engagement Through ChatGPT
- Chapter 3 A Practical Study of Generative AI Tools for Higher Education Innovative Transformation
- Chapter 4 Disrupting Education: Artificial Intelligence in Higher Education
- Chapter 5 The Role of Artificial Intelligence in Designing Higher Education Courses: Benefits and Challenges
- Chapter 6 Plotting the Parameter Scale: AI-Paved Academic Transformational Journey
- Chapter 7 Transforming Teaching Learning With Chatbots in Higher Education: Quest, Opportunities and Challenges for Quality Enhancement
- Chapter 8 Digital Transformation in Higher Education: Best Practices and Challenges
- Chapter 9 The Integration and Development of AI (Artificial Intelligence) in Higher Education (HE); Challenges, Innovations, and Recommendations for the Academics
- Chapter 10 Impact of Critical Thinking Approach on Learners' Dependence on Innovative Transformation Through Artificial Intelligence
- Chapter 11 Impact of Artificial Intelligence (AI) in Addressing Students at-Risk Challenges in Higher Education (HE)
- Chapter 12 AI for Higher Education: Alternative Ways of Learning and Risks
- Chapter 13 AI: Powering Sustainable Innovation in Higher Ed
- Chapter 14 A Triadic Approach to Generative AI Solutions for Educators in Transforming Higher Education
- Chapter 15 The Evolution of Artificial Intelligence in Teaching and Learning of English Language in Higher Education: Challenges, Risks, and Ethical Considerations
- Chapter 16 Exploring the Role of Generative AI Tools Among the Undergraduates of HEIs in Sultanate of Oman
- Chapter 17 Visioning the Future of Higher Education Through Artificial Intelligence