Now more than ever, the question of how to adopt Artificial Intelligence (AI) into higher education institutions. Universities around the world have either implemented certain AI tools in teaching, research, and administration or have begun to strategize how to best adopt these technologies and systems. The following guidelines are the most widely recommended considerations for institutions undergoing the process.
Planning According to Context
When implementing AI into any higher education institution, it must be done so in consideration of why the institution is following such a path. This should include considerations for evolving technologies, the importance of adapting to new learning methods, and the regional and global context in which such implementation is carried out. In other words, universities should adopt AI not just for the sake of doing it but so that it is addressing their particular needs and improving teaching, research, and administrative activities (Miranda et al, 2021).
The adoption of new technology in institutions always requires careful planning, assessment, and evaluation of the changes it entails. Implementing new technologies can bring numerous advantages if done properly and seen through to completion. To stay ahead, it is essential to plan and establish actions for improvement where it is needed in the institutions (Rico-Bautista et al, 2021).
Consultation with Technology Experts
Once the needs of the institution are identified, universities should consult with technology experts to plan for how best to address them using AI systems. At this stage, there should be a team of experts with the necessary skills (especially technological) who will oversee the implementation process and select appropriate partners for the university to work with. Education technology developers and service providers have a role in this process, as they can help design systems that incorporate human values and technological tools according to the specific needs of a university. Universities will require their help in developing systems that require less internet bandwidth to ensure accessibility among all students and staff. When selecting the company, it is important that universities understand what the company offers, how their systems integrate with existing infrastructure, and the adjustments required for use, all while meeting the needs of the university (Sharma et al, 2022).
The adoption of AI is a costly process, considering the cost of technological expertise, equipment and infrastructure, and training, not to mention the maintenance and constant upscaling of AI systems. For universities to strategically implement AI, they need to allocate funds carefully and with commitment to seeing the process through to completion (Sharma et al, 2022).
Acceptance of AI
Before introducing plans for AI implementation to professors and administrators, it is first recommended that universities survey their general attitudes towards AI. It is important that professors understand how AI is beneficial to them in terms of both teaching and research, highlighting AI’s supporting role in their jobs. Their understanding, acceptance, and support of AI is a crucial factor in the adoption process. Professors’ attitudes towards adopting AI technologies plays a significant role in the effectiveness of implementation, as they will ultimately decide how integrated AI will be in the classroom and in supporting teaching activities outside the classroom.
To facilitate acceptance of AI technologies among professors, it is essential to not only emphasize the usefulness of these technologies, but also to reduce anxiety caused by unfamiliarity and feelings of unreadiness. Instilling professors with confidence in their ability to perform tasks and achieve teaching goals is also necessary in their acceptance of AI. Individual differences among professors should be considered, making it important to find effective ways to address their anxieties and strengthen their confidence in adopting AI technologies (Wang et al, 2021; Sharma et al, 2022).
AI acceptance among students is also an important factor to consider before implementing it. On the administrative side of universities, chatbots can be used to fulfill student needs by answering questions. Students accept chatbots when they believe that they can provide accurate answers and that they meet their needs more effectively than traditional communication methods. If students perceive that chatbots contribute positively and prevent ethics issues in customer service, they are more likely to continue using chatbots. Trust in chatbot usage directly impacts users’ intention to use the technology. Therefore, universities must make sure that there is sufficient data when introducing chatbots onto their websites to make it convenient for students and to make it a worthy investment (Rahim et al, 2022).
Training Professors and University Staff
Providing training for professors and support from technology professionals can help professors integrate AI technologies into their teaching more efficiently. In terms of teaching, it is vital for university professors to receive training in the use of new technologies. This enables them to effectively use these tools effectively and efficiently in their educational practices. Additionally, training professors and university staff in what to expect from AI in higher education can ease concerns that AI will make their jobs obsolete and replace them. Professors with a higher confidence in their knowledge of AI allows them to effectively integrate technology into their teaching without insecurity. Therefore, providing practical training that focuses on the pedagogical use of AI, emphasizing how it will improve education and research, and encouraging the perceived usefulness of AI technologies will give professors the motivation to adopt the technologies more easily (Wang et al, 2021; Rico-Bautista et al, 2021; Sharma et al, 2022).
To best implement AI in universities, it is important that proper infrastructures are in place to address teaching and learning needs, as well as to support activities outside of the classroom. Classrooms must be equipped with the proper resources to ensure that AI can be used to its most potential and to provide an accessible learning environment to students who otherwise cannot access such resources on their own. Beyond the classroom, it is also important to provide spaces for students to be able to use the technology, libraries, study rooms, and learning commons being a few examples. Such spaces should be provided for students, professors, and university employees alike (Miranda et al, 2021).
Most crucially, digital infrastructures must be in place for AI to best be adopted. It is important to analyze both the pros and cons and make a final determination on whether the implementation is feasible. Key factors to consider include the capacity of the institution’s infrastructure to handle the current and future data traffic. Network planning and forecasting are crucial steps to ensure the quality of service (Rico-Bautista et al, 2021). The importance of proper infrastructure also influences attitudes towards AI. For example, the ability of the AI systems implemented to perform as expected influences university students’ intention to use AI-supported services such as chatbots. Facilitating conditions, which include the organizational and technical infrastructure to support technology use, affects students’ willingness to use chatbots in the future (Rahim et al, 2022).
Development of an Ethics Framework
Before launching AI tools in classrooms, universities should develop an ethics framework that addresses concerns over academic integrity and security. Professors should encourage students to use AI tools such as ChatGPT creatively and critically to enhance and expand their own texts, while discouraging its use for plagiarism. As AI applications continue to rapidly develop and integrate into widely used products, it becomes clear that utilizing AI applications in the university context is inevitable. Instead of implementing restrictive policies, universities and educators should focus on promoting responsible use and addressing potential challenges associated with AI tools (Gimpel et al, 2023).
The use of data in AI systems may be a cause for concern among university staff, which is why it is important to promote proper consultation and reassurance about the benefits and data processing are important to address staff concerns and foster adoption. The way data is handled and the security measures in place directly impact the level of trust placed in the technology. Universities must demonstrate responsibility by developing data systems in an ethical manner, showing care for the use of, processing, and sharing of data, while protecting individuals’ data (Sharma et al, 2022; Rico-Bautista et al, 2021).
Transparency is also key to developing an ethics framework. Building trust and encouraging adoption of AI systems requires effective communication. Universities should communicate the purposes of AI systems and their benefits to citizens through various channels such as email, websites, social media, and other collaborators (Sharma et al, 2022).
For professors in particular, before utilizing ChatGPT (or other such tools) as a teaching tool, it is crucial to define the specific learning objectives of their courses. Higher education learning objectives can vary depending on the field of study and subject matter. Professors’ ability to create and refine prompts tailored to the desired tasks or goals is essential for achieving the desired outcomes. Therefore, it is important that professors spend time developing expertise in using ChatGPT for their specific subjects and how they will teach students to utilize it in the same way (Gimpel et al, 2023).
Implementing AI for Specific Disciplines
The adoption of AI into universities will vary by discipline. While the practices above are recommended in general for all universities, the following are specific to medical, accounting, and engineering education.
Mentioned above is the need for a team of experts to oversee the implementation of AI into universities. In medical education, it is important that these teams include experts from across different disciplines. These include “educational experts, data scientists for management of the large pool of data, physicians for ensuring clinical relevance,” as well as engineers who will focus on “the accuracy of the AI system.” This collaboration will ensure that both the technological and discipline-specific expertise is being considered (Chan, 2019).
Importantly, medical education students and professors require incredible trust in the AI systems they will use in classrooms. As such, it is important that the technology is easy to use, and that the usefulness of a specific tool is explained and matched to a particular function in medicine. It is significant that there is a positive perception of AI among students and staff, not just of the technology itself but in how universities handle it from “managerial, organisational, operational, and IT infrastructure” perspectives (Sitthipon et al, 2022).
Universities can introduce AI into accounting programs to address the issue of technology skills gaps in the accounting industry. Accounting students’ willingness to embrace new technology is critical to the adoption of AI in accounting education. Before investing in AI applications in curricula, it is critical to do research to assess accounting students’ technological readiness. Universities can facilitate rapid technology adoption and better prepare students for the future by devising course implementation strategies that take into account students’ technological readiness. Collaboration with AI software companies can help to accelerate the adoption of courses that explore the application of AI in accounting, providing insights into new technology skills required for future accountants and auditors. These courses shape students’ impressions of AI’s value and ease of use, ultimately pushing AI adoption in accounting education (Hassan & Anwar, 2021).
Implementing AI in engineering education requires emphasis on “virtual and digital infrastructures,” which support students’ learning inside and outside the classroom. Highlighted is the essentialness of tools such as “virtual classrooms, online libraries, instant messaging systems, and remote laboratories” to ensure support outside of the classroom. Not only that but providing spaces for engineering students to use AI tools collaboratively is important to supporting the learning process.
Furthermore, teaching methods must adapt to encourage challenge- and problem-based learning, giving students practical experience with the technologies provided. Introducing AI can address the problem in developing skill competencies in students, but this approach functions best when universities focus on centering students. In other words, engineering students should be actively involved in determining how AI technologies will improve their education through in-class activities (Miranda et al, 2021).
In conclusion, the integration of Artificial Intelligence (AI) in higher education institutions requires careful planning, consultation with technology experts, financial considerations, and the promotion of acceptance among professors and students. Training, infrastructure development, the establishment of an ethics framework, and discipline-specific considerations are crucial for successful implementation. By following these guidelines, universities can leverage AI to enhance teaching, research, and administrative activities, ultimately providing a more effective and engaging learning experience for students while addressing the unique needs and challenges of their institutions.
Chan, Kai Siang, and Nabil Zary. „Applications And Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review.” JMIR medical education 5, no. 1 (2019): e13930. https://mededu.jmir.org/2019/1/e13930.
Gimpel, Henner, Kristina Hall, Stefan Decker, Torsten Eymann, Luis Lämmermann, Alexander Mädche, Maximilian Röglinger et al. “Unlocking the Power of Generative AI Models and Systems Such as GPT-4 and ChatGPT for Higher Education: A Guide for Students and Lecturers.” Hohenheim Discussion Papers in Business, Economics and Social Sciences, No. 02-2023. https://www.econstor.eu/handle/10419/270970.
Hassan Damerji & Anwar Salimi. “Mediating Effect of Use Perceptions on Technology Readiness and Adoption of Artificial Intelligence in Accounting.” Accounting Education 30, no. 2 (2021), 107-130.10.1080/09639284.2021.1872035.
Miranda, Jhonattan, Christelle Navarrete, Julieta Noguez, José-Martin Molina-Espinosa, María-Soledad Ramírez-Montoya, Sergio A. Navarro-Tuch, Martín-Rogelio Bustamante-Bello, José-Bernardo Rosas-Fernández, and Arturo Molina. „The Core Components of Education 4.0 in Higher Education: Three Case Studies in Engineering Education.” Computers & Electrical Engineering 93 (2021): 107278.https://www.sciencedirect.com/science/article/pii/S0045790621002603.
Rahim, Noor Irliana Mohd, Noorminshah A. Iahad, Ahmad Fadhil Yusof, and Mohammed A. Al-Sharafi. „AI-Based Chatbots Adoption Model for Higher Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach.” Sustainability 14, no. 19 (2022): 12726. https://www.proquest.com/openview/1d562c23afbd0bd5ba1fe88e81fd4bf2/1?pq-origsite=gscholar&cbl=2032327.
Rico-Bautista, D., Medina-Cardenas, Y., Coronel-Rojas, L.A., Cuesta-Quintero, F., Maestre-Gongora, G., Guerrero, C.D. “Smart University: Key Factors for an Artificial Intelligence Adoption Model.” Advances and Applications in Computer Science, Electronics and Industrial Engineering, vol 1307. Springer, 2021. https://doi.org/10.1007/978-981-33-4565-2_10
Sitthipon, Tamonwan, Pichart Kaewpuang, Parichat Jaipong, Patcharavadee Sriboonruang, Sutithep Siripipattanakul, and Pichakoon Auttawechasakoon. „Artificial Intelligence (AI) Adoption in the Medical Education during the Digital Era: A Review Article.” Review of Advanced Multidispliniary Science, Engineering & Innovation 1, no. 2 (2022): 1-7. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4168693
Sharma, Himani, Taiwo Soetan, Temitope Farinloye, Emmanuel Mogaji, and Miguel De Freitas Noite. „AI Adoption in Universities in Emerging Economies: Prospects, ChallengesaAnd Recommendations.” In Re-imagining Educational Futures in Developing Countries: Lessons from Global Health Crises, pp. 159-174. Cham: Springer International Publishing, 2022. https://link.springer.com/chapter/10.1007/978-3-030-88234-1_9.
Wang, Youmei, Chenchen Liu, and Yun-Fang Tu. “Factors Affecting the Adoption of AI-Based Applications in Higher Education: An Analysis of Teachers Perspectives Using Structural Equation Modeling.” Educational Technology & Society 24, no. 3 (2021): 116–29. https://www.jstor.org/stable/27032860.