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Emergent Role of Artificial Intelligence in Higher Education

By Christopher Nouhan, Noah Scott, James Womack, Ball State University, Indiana, US

May 2021

Artificial Intelligence (AI) has been seamlessly integrated into our lives. In 2016, Barack Obama reported, “the walls between humans and AI systems are slowly beginning to erode, with AI systems augmenting and enhancing human capabilities. Fundamental research is needed to develop effective methods for human-AI interaction and collaboration [1].” As institutions of higher education continue to grapple with the impacts of COVID-19, some are turning to existing technologies to make educational experiences safer, more efficient, and more adaptable to changing environments. AI in higher education (AIEd) enables institutions to have meaningful impacts on teaching and learning environments. When using the term AI in relation to higher education, it can be described as an information system that acts as if it were intelligent by perceiving and acting upon its environment. The bandwidth of teaching professionals has been grossly misidentified, and as remote learning methods become more common, there will be deeper discussions surrounding the role of AI in supporting these professionals. It will benefit both teachers and their students if instructors have more time to focus on curriculum contents and prolonged engagement, while AI applications take over administrative functions that are currently expected of teaching professionals.

Administrative Applications

When approaching AIEd from an administrative point of view, it is obvious that various tasks can be completed by AI more efficiently than a human counterpart. Tasks such as plagiarism detection, assessments, administration, and feedback can be automated, allowing for an increase in productivity– “teacher-facing AIEd can help teachers to reduce their workload, gain insights about students, and innovate in their classroom [2, pp. 12].” Furthermore, as data is collected from assessments and studies, AIEd can better predict student success and failures, along with suggesting curriculum to educators. These systems can “curate and stagger learning materials based on a student’s needs, diagnosing strengths, weaknesses, or gaps in a student’s knowledge, providing automated feedback, and facilitating collaboration between learners [2, pp. 11].” AI career coaches are examples of an existing matching system. They are able to provide personalized advice for students based on their academic careers and backgrounds [3, pp. 69]. The AI system begins intaking data from a students’ first interaction with the system, such as a submitted application or letter of interest. These AI systems can also develop term schedules for students, alongside an array of other tasks from the inputted data.

Instructor-Facing Application

AIEd enables teachers to reduce workloads, gain insight into students, and innovate their classrooms through instructor-facing systems [2, pp. 12]. These systems give educators more time, energy, and resources to dedicate to their primary role as an instructor, rather than secondary occupations that revolve around administrative tasks. This gives educators the power to utilize these systems as they see fit, allowing for greater control and influence. “Teachers will be the orchestrators of when and how to use these AIEd tools. The data-driven insights these tools provide will empower teachers to decide how best to marshal the various resources at their disposal [4].” Educators are even using AI to develop predictive models that identify low student engagement [5] and are able to predict dropout rates. This has allowed teachers to intervene and support students, which dramatically improved retention rates [6]. These AI-driven insights help create more engaging and successful learning environments. AI Teacher Assistants (AITA), or Teacherbots, handle administrative tasks such as content delivery, feedback, and supervision [1, pp. 9]. AITAs improve the student experience while simultaneously cutting down the administrative work of the traditional Teaching Assistant (TA). These solutions have presented new possibilities and an opportunity to rethink the role of teaching. As AIEd continues to evolve, we will soon be able to “provide just-in-time information about learner success, challenges, and needs that can then be used to shape the learning experience itself [4, pp. 35].” At Georgia Tech’s online master’s in computer science program, the teaching assistant was nominated “Outstanding TA of the Year” for students to later discover their TA “was not human, but a teacherbot based on the IBM’s Watson platform [1, pp. 9].” These AI systems have “widespread implications for the advancement of AI, to the point where computers can serve as personalized tutors able to guide and manage students’ learning and engagement [3].”

Student-Facing Application

Automation and personalization in AI enable advanced learning opportunities for students, and greater control for teachers. There are a variety of systems that can be categorized as student-facing AIEd, including software that students use such as chatbots and intelligent tutoring systems (ITS), which contribute to student learning and collaboration. An AI-enabled chatbot is built to mimic a real conversation. It provides students with solutions to their individual questions in various ways such as forum monitoring, self-paced learning, or as tutoring assistants. AITAs are used in a way that enables them to provide outside student assistance from regular classes [7]. As the system gathers more information and matures, so will the accuracy of the answers it provides. An ITS works to constantly learn the behaviors of the students they support to ensure models and lessons are adjusted to what is most applicable [8]. AIEd can create environments that foster collaboration between students, especially as many students are limited to learning remotely, in a virtual capacity. ITSs are focused on the students’ learning experience and mastery of the material.

Future Outlook

As with any major innovation in life, it is important to understand the ethical dilemmas associated with this. Many fear that AI task automation will eliminate or replace jobs, and many feel humans need personal connections to help support and guide their learning experience. AI lacks the emotional and social support humans need in an academic environment. However, the tasks that AI automates enables teachers to enhance the cognitive ability of the student, and the ability to control information. This will cause a shift in job roles, resulting in a reduced workload and an enhanced student learning experience. It is argued that “the real potential of technology in higher education is, when properly used, to extend human capabilities and possibilities of teaching, learning, and research [1, pp. 3].” AI advancements are already being implemented into higher education through the automation of simple, repetitive tasks. The AI systems use ‘machine learning’ to apply “newly discovered patterns to situations that were not included or covered by their initial design [1].” As AI systems learn more advanced solutions, humans will be required to sort the data collected, causing a shift in the roles of higher education. There is a potential large-scale transformation among educators that will eliminate time-consuming administrative tasks and enable them to “devote more energy to the creative and very human acts that provide the ingenuity and empathy needed to take learning to the next level [4, pp. 31].” Teachers who welcome the innovation and fluency of AIEd will be at the forefront of technological literacy, learning advanced skills, and pioneering the future of these systems.

References 

  1. S. A. D. Popenici and S. Kerr, “Exploring the impact of artificial intelligence on teaching and learning in higher education,” Research and Practice in Technology Enhanced Learning, 23-Nov-2017. [Online]. Available: https://telrp.springeropen.com/articles/10.1186/s41039-017-0062-8. [Accessed: 4-Dec-2020].
  2. T. D. Baker and L. Smith, “[PDF] Educ-AI-tion Rebooted ? Exploring the future of artificial intelligence in schools and colleges: Semantic Scholar,” [PDF] Educ-AI-tion Rebooted ? Exploring the future of artificial intelligence in schools and colleges | Semantic Scholar, 01-Jan-1970. [Online]. Available: https://www.semanticscholar.org/paper/Educ-AI-tion-Rebooted-Exploring-the-future-of-in-Baker-Smith/d01f96c020e0b1f72df9a55c93fd7672001a8e91. [Accessed: 04-Dec-2020].
  3. K. Khare, B. Stewart, and A. Khare, “Directory of Open Access Journals,” IAFOR Journal of Education, 01-Dec-2018. [Online]. Available: https://doaj.org/article/450cfce9e115445392ef297f99b911d8. [Accessed: 04-Dec-2020].
  4. R. Luckin and W. Holmes, “Intelligence Unleashed: An argument for AI in Education,” UCL Discovery – UCL Discovery, 22-Feb-2016. [Online]. Available: https://discovery.ucl.ac.uk/id/eprint/1475756/. [Accessed: 04-Dec-2020].
  5. M. Hussain, W. Zhu, W. Zhang, and S. M. R. Abidi, “Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores,” Computational Intelligence and Neuroscience, 02-Oct-2018. [Online]. Available: https://www.hindawi.com/journals/cin/2018/6347186/. [Accessed: 04-Dec-2020].
  6. S. Jayaprakash, E. Moody, E. Lauria, J. Regan, and J. Baron, “Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative,” View of Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative. [Online]. Available: https://learning-analytics.info/index.php/JLA/article/view/3249/4011. [Accessed: 04-Dec-2020].
  7. S. Chatterjee, “Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling,” Shibboleth Authentication Request. [Online]. Available: https://link-springer-com.proxy.bsu.edu/article/10.1007/s10639-020-10159-7. [Accessed: 04-Dec-2020].
  8. S. Steenbergen-Hu and H. Cooper, “A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning.,” American Psychological Association. [Online]. Available: https://psycnet.apa.org/record/2013-44233-001?errorCode=invalidToken. [Accessed: 04-Dec-2020].

 

Christopher Nouhan is a graduate student at Ball State University, studying at the Center for Information and Communication Sciences, a top-25 nationally ranked information technology program. He is the co-founder of N4 Studio, a digital marketing startup, and is fascinated with bridging people and technology to solve problems.

Noah Scott is a lifelong learner working towards connecting people and new technologies. At Ball State University’s Top 25 Nationally Ranked Information Technology Program, he is a graduate assistant at the Center for Information and Communication Sciences. As a Telecommunications undergraduate alumnus, he looks to utilize his storytelling skills to convey complex ideas and methodologies in a simplistic manner.

James P. Womack is a graduate student in the Ball State University’s Center for Information and Communication Sciences with an undergraduate degree from the Entrepreneurial Management from the Miller College of Business. He is a lifelong learner who is always looking for new trends and opportunities that can be capitalized on to help make the world a better place.

Editor: 

Tahir Hameed is with Merrimack College since 2018 where he teaches courses related to information systems. Prior to joining Merrimack, Dr. Hameed was associated with SolBridge International School of Business in South Korea from 2012 to 2018 where he taught in the areas of information systems, technology management, and business analytics at the masters and bachelors levels. Dr. Hameed obtained his Ph.D. in Information Technology Management from the Korea Advanced Institute of Science and Technology (KAIST) and obtained his Masters in Computer Science from Lahore University of Management Sciences (LUMS). His current research focus is in the areas of health analytics, health IT, IT standards, technology commercialization, IT-enabled change management, and knowledge management. He has published in prestigious journals such as Computers in Human Behavior, Sustainability, Journal of Knowledge Management, Telecommunications Policy, Technological Forecasting, and Social Change and World Development. He has presented several papers at leading conferences including the International Conference on Health Informatics, IEEE conference on Industrial Engineering and Engineering Management, and Australasian Conference on Information Systems.