Can artificial intelligence transform higher education? What does research say?

The answer

Not yet.

The research

Springer Open has just published the editorial of the International Journal of Educational Technology in Higher Education’s special issue on artificial intelligence in higher education.

The aim of this edition was to examine the potential and actual impact of artificial intelligence (AI) on teaching and learning in higher education.

I was part of the editorial team, which consisted of:

• Cristóbal Cobo, 3Education Global Practice, World Bank, Washington, D.C., USA
• Olga Mariño, 4FLAG-TICSW Research Group, Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
• Steve Wheeler, Plymouth Institute of Education, Plymouth, UK.

In all, a total of 23 articles submitted met the criteria for selection, but after review, only four of the 23 articles were considered appropriate for publication, based on their academic quality.

In other words, only four of the submitted articles provided sound empirical evidence about the effect of AI applications on teaching and learning in higher education and one of these was a (thorough) review of the previous literature (Zawacki-Richter et al.), rather than a specific study itself. Zawacki-Richter’s review was based on 143 papers that met the same criteria of evidence-based research.

Main findings

As always I recommend readers to go to the actual publication and read carefully the editorial and selected papers and draw your own conclusions, but here are my views.

It should be noted that the focus was specifically on teaching and learning, not on other educational applications of AI, such as admission enrolment or general student guidance. Nevertheless, the lack of quality empirical research on the impact of AI on teaching and learning in higher education is itself a disappointing result.

Of the four accepted articles:

• Zawacki-Richter at al. provide an overview of the various areas where AI is being applied in higher education, as well as an indication of which areas researchers have tended to focus on. From these 146 articles, they were able to identify four key areas of AI applications for teaching and learning:

  • profiling and prediction

  ▪ intelligent tutoring systems

  ▪ assessment and evaluation

  ▪ adaptive systems and personalisation

• Akçapinar, Altun and Askar observed that 74% of the students who were unsuccessful at the end of term in an online computer science course in Turkey could be accurately predicted through the use of a specific algorithm (kNN) in as short as 3 weeks from the beginning of the course.

• Tsai et al. were able to identify students entering higher education in Taiwan with a high risk of subsequent drop-out, and the factors associated with high risk of drop-out, enabling intervention strategies to be developed.

• Renz and Hilbig found that the current use of learning analytics and artificial education in the field of further education is at only a preliminary stage, mainly due to a lack of demand from educational institutions, and they propose some of the reasons for this.

The editorial team concluded:

Artificial intelligence is in widespread use in some areas of society. In its direct impact on teaching and learning [in HE] though, much has been promised, but as yet, little has been achieved. From the articles submitted, few showed any evidence-based significant influence of AI on teaching and learning in post-secondary or higher education. The main impact was on the prediction of student success or failure. There was no valid evidence of improved learning outcomes, or radical, or even tangential pedagogical changes resulting from AI applications [within the papers submitted].

Why?

The editorial team offered a number of reasons for the lack of evidence.

One obvious one is that those researching AI applications in teaching and learning do not publish mainly in educational journals. Indeed both the Zawacki-Richter study and the papers submitted for this edition of the IJTHE journal were mainly from computer scientists rather than educators. There are more papers in the area of learning analytics, but  applications of LA are often not directly aimed at the teaching and learning process. The lack of educators involved in the submission of papers and in Zawacki-Richter et al.’s review was quite startling.

It is also likely that there are many other applications of AI to teaching and learning, but these are not being systematically evaluated at a level sufficient for publication in an academic journal focused on teaching and learning.

One of the main conclusions the editorial team came to was that educators and computer scientists need to work together in the application of AI to teaching and learning. Many of the applications by computer scientists are based on very narrow views of learning, and focus primarily on validating the algorithms rather than looking at the potential impact of AI on the learning process. Pedagogy-free research is unlikely to have much impact on higher education teaching practice.

What next?

However, the editorial also recognised that it was naive to think that AI applications were being done to support the current system of teaching and learning, but were more likely in the future to focus on replacing or commercialising higher education learning and teaching. If so, there is still a long way to go.

However, although it hasn’t been successful to date, AI still has the potential to disrupt the system. The editorial concludes that AI is a sleeping giant. Educators ignore it at their peril. But do go to the journal and read the editorial and papers for yourself.

References

Akçapinar, G., Altun, A. and Askar, P. (2019) Using learning analytics to develop early-warning system for at-risk students International Journal of Educational Technology in Higher Education, 16:40 

Bates, Y, Cobo, C., Mariño, O, and Wheeler, S. (2020) Can Artificial Intelligence Transform Higher Education, International Journal of Educational Technology in Higher Education, 14:42 

Renz, A. and Hilbig, R. (2020) Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies International Journal of Educational Technology in Higher Education, 17:14

Tsai, S-C., Chen, C.-H., Shiao, Y.-T., Ciou, J.-S., and Wu, T.-N. (2020) Precision education with statistical learning and deep learning: a case study in Taiwan International Journal of Educational Technology in Higher Education, 17:12