As I write this essay, ChatGPT has dominated the front pages of newspapers for months. It has been at the center of discussions in the newsrooms and talk-show studios of television networks around the world, and has captured the interest and imagination of many online contributors. ChatGPT is an AI chatbot claimed to be able to write essays, compose songs and poems, and solve complex math problems. It can follow instructions, but also give advice and explain complex phenomena. Its capabilities prompted many to proclaim the end of homework, announce that human educators are becoming obsolete, and declare that a revolution in education is coming (Grove, 2023; Huang, 2023; Mitchell, 2023; Roose, 2023; Weale, 2023). Even though ChatGPT is one of the most advanced AI-based technologies for educational purposes, the utopian and dystopian scenarios, the dread and hope, surrounding it have been common reactions to many digital technologies that have become available over the last decades (Facer & Sewlyn, 2021). In this essay, I explore how the use of digital, including AI-based, technologies are expected to shape how we approach and provide education. I first discuss the positive expectations1 surrounding digital technologies, after which I briefly map the main negative expectations that have been voiced. I end by advancing a number of changes and adjustments that will be needed to ensure the successful use of digital technologies in education.
The development and use of digital technologies in education have often been framed as an effective solution to some of the most pressing challenges plaguing this sector (Haleem et al, 2022; Van Dijk et al, 2018; Vincent-Lancrin and van der Vlies, 2020). In general, digital technologies are expected to make three main sets of contributions, some of which have already been partially realized. First, they are thought to render education more cost-effective and agile, thereby allowing for the provision of relevant and good quality instruction amidst a decrease in available funding even in well-off countries (Education Finance Watch, 2022). Second, digital technologies are expected to render education more accessible to diverse students in a context marked by an insufficient number of educators and by significant differences in their distribution across cities, countries, and world regions (Jack and Cocco, 2022; Schmitt and DeCourcy, 2022). Thirdly, but equally importantly, these technologies are thought to enhance the personalization of education, thereby improving how students experience their education and develop their skills and knowledge (European Commission, 2013; Papamitsiou et al, 2014). In what follows, I discuss each of them in more detail.
Digital technologies are expected to help render education more cost-effective and agile by taking over some of the administrative and organizational tasks of teachers and by facilitating a quicker and more affordable development and update of educational materials2 (Chen et al, 2020; Nouri et al, 2019; Spice, 2020). They already make important contributions in this respect, as digital technologies are often used to check whether grades are correctly registered, to indicate whether or not individual students meet the mandatory requirements at a specific stage in their educational trajectories, to send teachers and students reminders about upcoming courses, lectures, and exams, and to facilitate planning and (re)scheduling (Holmes et al, 2019; Selwyn et al, 2023). Digital technologies also make it easier to develop educational materials by allowing for quick access to the latest information and scientific studies and by enabling the translation of these insights into dynamic and diverse lecture content, and into appealing and relevant tutorial assignments and group tasks (Lockyer et al, 2013; Bennett and McWhorter, 2020). All of these are meant to assist students in the acquisition of so-called ‘21st century knowledge and skills’ (Ng, 2015). Noteworthy in this sense is the development and use of multi-modal materials that digital technologies facilitate. Thus, next to analog written texts and images, videos, podcasts, and excerpts from diverse online platforms have become habitual components of most courses nowadays. So have longer or shorter digital encounters with international experts in a given field, as digital technologies have allowed for the easier involvement of guest lecturers. Developments in virtual reality, augmented reality, and related fields have also enabled teachers to alternate between physical and virtual encounters in their teaching activities (Ng, 2022). For instance, many teachers have hosted lectures in Second Life, where they could share their insights in virtual environments equipped to meet the specific needs of a particular topic or module.
An important innovation is the introduction of digital technologies as additional tutors, meant to complement the human teachers (Belpaeme et al, 2018; Holmes et al, 2019, Ng, 2022). Chatbots and humanoid robots in particular have started to assist in classrooms by giving demonstrations, by providing additional explanations, and by training students in the development of specific skills, such as debating, communication and conflict resolution, or the correct recognition and interpretation of emotions. Digital technologies have further helped render lectures and tutorials more interactive by allowing for the quick administration of tests and surveys. They have also contributed to the use of multiple forms of examination, with students being required to write essays, to answer multiple choice questions, and to conduct group work as part of their assessment for one and the same course. More recently, AI-based technologies have started to be used for grading (Jackson and Panteli, 2023), and they are claimed to display satisfactory abilities to correctly assess multiple-choice exams, and, to a more limited extent, open exam questions and essays (Kumar and Boulager, 2020).
Another important expectation concerning digital technologies is that they will help improve access to education for highly diverse groups of students (The Association of Commonwealth Universities, 2021; Chawinga and Zozie, 2016; Zhang, 2006). Thus, digital technologies are often claimed to enable people from remote areas, where no schools and universities exist, or where the number of teachers is insufficient, to complete online education programmes. Furthermore, digital technologies are thought to allow students who are dissatisfied with the level or type of education provided in their environs to follow the courses and training made available by prestigious universities and institutions. From this point of view, digital technologies are expected to contribute to diminishing an important source of inequality by allowing a growing number of individuals to further their theoretical knowledge, to develop their communication and collaborative skills, and to become part of vibrant international communities (Ng, 2015). Digital technologies are further expected to facilitate access to education for people with different bodies and health states (Fahimirad, 2018; Kent, 2015). In such instances, a lot of attention is paid to the affordances of these technologies, which are thought to make it more convenient to modify and diversify study materials. From this point of view, digital technologies are thought to facilitate the availability of study materials in Braille, the real-time automatic captioning of audio content, and the quick adjusting of the sensorial characteristics of study materials, to avoid triggers or to enhance focus and retention.
The other main contribution that digital technologies are expected to make is to substantially improve how students experience their education and develop skills and knowledge (Ayoub, 2020; Maull et al, 2014; Ng, 2015). After many years of speculation, recent developments in AI-based technologies suggest that we may soon witness the rise of digitally-informed personalized education (Luckin et al, 2016). Digital technologies are envisioned as contributing to the provision of personalized education by facilitating the quick development of materials that match the needs and preferences of individual students (Chen et al, 2020; Holmes et al, 2018; Mead, 2016). In several instances, AI-based technologies have already been able to create multiple versions of texts on a given topic, and more technologies are expected to be able to do so in the near future. These technologies are not only capable of using more and less complex vocabulary, but also of adapting the examples they give to stimulate students’ curiosity based on their specific proficiency, hobbies and passions (Mead, 2016). Whereas currently the level of concentration and interest of students are determined mainly based on their class participation, their grades and course evaluations, AI-based technologies are expected to provide dynamic, real-time information in this regard (Holstein et al, 2018). Thus, these technologies are envisaged as alerting teachers during lectures when the students’ concentration decreases, so that they can promptly intervene. In more utopian scenarios, such technologies are expected to be able to intervene autonomously, by suggesting to students materials, exercises, and tasks that are adjusted for their level of energy and concentration. They are even envisaged as continuously adapting the level and content of instruction to an individual’s health and mental state.
Even though, at the moment, it seems that the appeal of digital technologies in education outweighs the dread they trigger, it is important to map the main negative expectations and dystopian visions they have informed. Thus, some of their opponents have warned that having digital technologies always available to answer one’s questions or to solve one’s physics problems may impede students from developing independent and creative thinking and may reduce their ability to work independently (Attick, 2013; Downes, 2016; Turkle, 2007). More dramatically, others have announced that lecturers and tutors will be replaced by digital technologies in the not-too-distant future, and have expressed grave doubts about the abilities of robots and algorithms to assist in the development of moral, responsible, and politically engaged citizens (Biesta, 2012). In a related vein, some critics have worried that the intensive use of digital technologies in education may lead to anomie, as students may become isolated from their communities and may have a hard time relating to the troubles of others (Lynch, 2017). Others have noted that digital technologies increase the potential for student fraud, and have warned that they may function as agents of moral corruption, allowing students to deceive themselves and others about their capacities. Yet others have gloomily predicted that digital technologies will in time do away with whole disciplinary fields, thereby preventing students from developing knowledge and skills that will help them thrive professionally, even though their impact is more difficult to monitor and quantify (Popenici, 2022). Perhaps the most dystopian vision comes from critics who conceive of the use of digital technologies in education as leading to future generations of adults liable to outside influences due to the extensive personal profiles developed through the collection of vast education data. By continuously tracking the individuals’ educational development, their reactions to different topics, and their approaches to various tasks and challenges, it may not only become possible to accurately predict their behavior, but also to steer it (Van Dijk et al, 2018).
The utopian and dystopian visions sketched above are not only fascinating but also illuminating, as reflection upon them points to the changes and adjustments that will be needed to ensure the successful use of digital technologies in education. Digital technologies have great potential, but they will not be able to provide the solutions we have been waiting for on their own. On the contrary, the positive impact of these technologies will depend on the adequacy of the rules and regulations that will determine how they can be used, by the contexts in which they will be deployed, and by the values, choices, and preferences of their users. To ensure that digital technologies help render education more accessible, diverse, and personalized, the current legal framework will need to be amended to avoid additional or new sources of inequality and discrimination. Considerable attention will have to be paid to who will have the power and authority to decide what digital technologies will be used in universities (and any other educational institutions, for that matter) and on the values and criteria underlying these decisions. This is because there are great differences between digital technologies regarding their quality, their ease of use, the type of data that they collect, and the level of privacy and security they afford. At the same time, the price of these technologies and the cost of the infrastructural modifications needed for their deployment may mean that only some universities will be able to purchase them. There may therefore be important differences between these technologies, also concerning their affordability, which suggests that online education and digital tutors may not be equally accessible to all students. As examples from other sectors have already shown, less-endowed universities may be tempted to make agreements with owners of digital technologies, purchasing the latter at a low rate or acquiring them for free in exchange for the data that would be collected through them. Such approaches are bound not only to further the competitive advantage of Big Tech companies, which may more easily afford such deals, but also to lead to important inequalities among students regarding the ownership of their educational data.
Even though this does not seem to be often considered in media reporting, the successful deployment of digital technologies in education will also depend on the degree to which human lecturers and tutors will be able to accommodate them and adjust to them. Lecturers and tutors will need to develop new skills and types of expertise, and also to modify, to a greater or lesser extent, long-standing approaches to teaching and to student interactions. They will have to learn to identify some of the biases inherent in the algorithms powering these technologies and figure out ways to work around them. Similarly, they will need to develop effective approaches for handling the incorrect teachings that some of these technologies may once in a while bestow upon students. Furthermore, lecturers and tutors will need to have the patience and the emotional intelligence to try out recommendations put forward by AI-based technologies that may run counter to their hard-earned pedagogical experience. At the same time, they will be confronted with new challenges and responsibilities, as digital technologies may allow for the identification of social or behavioral problems among students that would have previously remained invisible. The adjustments that lecturers and tutors will need to make to help digital technologies live up to some of the expectations sketched above are bound to take a lot of time, effort, and commitment. Digital technologies may help render education more cost-effective, but this may take a (long) while.
Also, students will need to acquire new knowledge and adjust their attitudes and behaviors to help ensure the successful use of digital technologies in education. To begin with, the advantages of personalized education sketched above will be wrought through the continuous collection of student data by highly intrusive technologies. This may make it difficult to distinguish between education and surveillance, but students, along with lecturers and regulators, will need to learn to make this difference. Students will also have to become knowledgeable about their rights and about the approaches through which they can verify or ensure that their educational data will not be made available to third parties or will not be used in ways that may harm them. Another important point is that digital technologies may help provide students with an education that is appropriate and appealing to them, but they achieve this by overly simplifying and quantifying complex aspects of human thinking and being, such as drive, curiosity and concentration. This means that students may further some of their skills and knowledge, but they may also have fewer opportunities to develop grit, resilience and tolerance to boredom. Unappealing though these may seem, they are integral to the substantial and long-lasting development of skills and knowledge and to living a fulfilling life. Students will therefore need to have the maturity and creativity needed to pursue the development of these traits in different ways.
Students will also need to make efforts to acknowledge the intrinsic value of learning, given that most digital technologies (that will be) deployed in education have the tendency to gamify instruction and to use reward systems to prompt them to complete their modules or courses. Furthermore, at a time when the ability to communicate and work together with diverse people is highly needed, students may have fewer opportunities to develop such skills through their encounters with colleagues. This is because an integral element of personalized education is the expectation that digital technologies will bring together students with similar interests and/or learning styles, which is thought to render learning more efficient. This may severely limit the knowledge and skills that students acquire, given that a lot of our insights and abilities are sharpened through interactions with people who challenge us, and who think differently than us. Whereas a few centuries ago traveling to different countries and seeking all sorts of adventures was an integral part of a young man’s (sic!) Bildung, the use of digital technologies may require that students make substantial efforts to encounter and engage with difference and diversity outside of staged and technologically mediated environments.
To conclude, there is no doubt that digital technologies will shape the future of education. What this future will look like is bound to depend on our ability to reflect and to forge a path between the utopian and dystopian visions surrounding these technologies. A useful first or second step may be acknowledging what we have taken for granted in our lecturers and tutors, and what we have thus far considered small and trivial in our learning and teaching interactions.
Attik, D. (2013). Education is Dead: A Requiem”. Critical Questions in Education, 5(1): 1-9.
Ayoub, D. (2020, February). Unleashing the Power of AI for Education. Wired, https://www.wired.com/brandlab/2020/03/unleashing-the-power-of-ai-for-education/
Belpaeme, T., Kennedy, J., Ramachandran, Scassellati, B., Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), DOI: 10.1126/scirobotics.aat5954
Bennett, E., McWhorter, R. (2020). Digital technologies for teaching and learning. In T. Rocco, M. C. Smith, R. C. Mizzi, L. R. Meriweather & J. D. Hawley (Eds.), Handbook on adult and continuing education (177–186). Stylus.
Biesta, G. (2012). Giving Teaching Back to Education: Responding to the Disappearance of the Teacher. Phenomenology and Practice, 6(2): 35-49.
Chawinga, W., Zozie, P. (2016). Increasing Access to Higher Education Through Open and Distance Learning: Empirical Findings from Mzuzu University, Malawi. International Review of Research in Open and Distributed Learning, 17(4), 1-20.
Chen, L., Chen, P., Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278.
Downes, S. (2016). Personal and Personalized Learning. https://www.downes.ca/post/65065
Education Finance Watch. 2022. The World Bank. Unesco. https://thedocs.worldbank.org/en/doc/5c5cdd4c96799335e263023fa96db454-0200022022/related/EFW-2022-Dec21.pdf
European Commission. (2013). Opening up Education: Innovative teaching and learning for all through new Technologies and Open Educational Resources. http://eurlex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52013DC0654
Facer, K., Selwyn, N. (2021, April). Digital technology and the futures of education -towards ‘non-stupid’ optimism. Background paper for the Futures of Education initiative. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000377071.locale=en
Fahimirad, M. (2018). A Review on Application of Artificial Intelligence in Teaching and Learning in Educational Contexts. International Journal of Learning and Development, 8(4): 106-118.
Grove, J. (2023, March 16). The ChatGPT revolution of academic research has begun. Times Higher Education. https://www.timeshighereducation.com/depth/chatgpt-revolution-academic-research-has-begun
Haleem, A., Javaid, M., Qadri, M., Suman, R. (2022). Understading the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285. https://doi.org/10.1016/j.susoc.2022.05.004
Hoeyer, K. (forthcoming). Data Paradoxes. The Politics of Intensified Data Sourcing in Contemporary Healthcare. MIT Press.
Holmes, W., Anastopoulou, S., Schaumburg, H., Mavrikis, M. (2018). Technology-Enhanced Personalised Learning: Untangling the Evidence. Robert Bosch Stiftung GmbH, Stuttgart. https://oro.open.ac.uk/56692/1/TEPL_en.pdf
Holmes, W., Bialik, M., Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. The Center for Curriculum Redesign.
Holstein, K., McLaren, B., Aleven, V. (2018). Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms. International Conference on Artificial Intelligence in Education, 154-168.
Huang, K. (2023, January 16). Alarmed by A.I. Chatbots, Universities Start Revamping How They Teach. The New York Times. https://www.nytimes.com/2023/01/16/technology/chatgpt-artificial-intelligence-universities.html
Jack, A., Cocco, F. (2022, September 2). Wanted: tens of thousands of teachers to staff Europe’s schools. Financial Times. https://www.ft.com/content/116d8c88-aa3f-426f-aeb8-c0a0325c43bb
Jackson, S., Panteli, N. (2023). Trust or mistrust in algorithmic grading? An embedded agency perspective. International Journal of Information Management, 69, 102555.
Kent, M. (2015). Access and Barriers to Online Education for People with Disabilities. The National Centre for Student Equity in Higher Education. https://www.ncsehe.edu.au/wpcontent/uploads/2016/05/Access-and-Barriers-toOnline-Education-for-People-with-Disabilities.pdf
Kumar, V., Boulanger D. (2020). Explainable Automated Essay Scoring: Deep Learning Really Has Pedagogical Value. Frontiers in Education, 5, 572367.
Lockyer, L., Heathcote, E., Dawson, S. (2013). Informing Pedagogical Action: Aligning Learning Analytics with Learning Design. American Behavioral Scientist, 57(10), 1439-1459.
Luckin, R., Holmes, W., Griffiths, M., Forcier, L. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson Education.
Lynch, J. (2017, November, 13). How AI will destroy education. Medium. https://medium.com/buzzrobot/how-ai-will-destroy-education-20053b7b88a6#:~:text=First%2C%20many%20AIEd%20algorithms%20rely,poor%20measures%20(see%20above)
Maull, R., Godsiff, P., Mulligan, C. (2014). The Impact of Datafication on Service Systems. 47th Hawaii International Conference on System Sciences, 1193-1201, doi: 10.1109/HICSS.2014.154
Mead, R. (2016, March 7). Learn different. Silicon Valley disrupts education. The New Yorker, https://www.newyorker.com/magazine/2016/03/07/altschools-disrupted-education
Mitchell, A. (2023, January 25). ChatGPT could make these jobs obsolete: ‘The wolf is at the door’. New York Post. https://nypost.com/2023/01/25/chat-gpt-could-make-these-jobs-obsolete/
Ng, D. (2022) What is the metaverse? Definitions, technologies and the community of inquiry. Australasian Journal of Educational Technology, 38(4), 190-205.
Ng, W. (2015) New Digital Technology in Education. Conceptualizing Professional Learning for Educators. Springer.
Nouri, J., Ebner, M., Ifenthaler, D., Saqr, M., Malmberg, J., Khalil, M., Bruun, J., Viberg, O., González, M., Papamitsiou, Z., Berthelsen, U. (2019). Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries. International Journal of Learning Analytics and Artificial Intelligence for Education, 1(1): 8-27.
Papamitsiou, Z., Economides,A. (2014). Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educational Technology & Society, 17(4): 49-64.
Popenici, S. (2022). Artificial Intelligence and Learning Futures. Critical Narratives of Technology and Imagination in Higher Education. Routledge.
Roose, K. (2023, January 12). Don’t Ban ChatGPT in Schools. Teach With It. The New York Times. https://www.nytimes.com/2023/01/12/technology/chatgpt-schools-teachers.html
Schmitt, J., DeCourcy, K. (2022, December 6). The pandemic has exacerbated a long-standing national shortage of teachers. Economic Policy Institute. Washington, DC. https://www.epi.org/publication/shortage-of-teachers/
Selwyn, N., Hillman, T., Rensfeldt, A., Perrotta, C. (2023). Digital Technologies and the Automation of Education – Key Questions and Concerns. Postdigital Science and Education, 5, 15-24.
Spice, B. (2020, May 6). New AI Enables Teachers to Rapidly Develop Intelligent Tutoring Systems. News. Carnegie Mellon University. https://www.cmu.edu/news/stories/archives/2020/may/intelligent-tutors.html
The Association of Commonwealth Universities. (2021). How can we use digital technologies to address inequalities in higher education? https://www.acu.ac.uk/news/how-can-we-use-digital-technology-to-address-inequalities-in-higher-education/
Turkle, S. (2007). How Computers Change the Way We Think. Routledge.
Van Dijk, J., Poell, T., de Waal, M. (2018). The Platform Society. Oxford University Press, 117-136.
Vincent-Lancrin, S., van der Vlies, R. (2020, April 6). Trustworthy artificial intelligence (AI) in education: Promises and challenges. OECD Education Working Papers, 218, https://doi.org/10.1787/19939019
Weale, S. (2023, January 13). Lecturers urged to review assessments in UK amid concerns over new AI tool. The Guardian. https://www.theguardian.com/technology/2023/jan/13/end-of-the-essay-uk-lecturers-assessments-chatgpt-concerns-ai
Zawacki-Richter, O., Marín, V., Bond, M., Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education -where are the educators? International Journal of Educational Technology in Higher Education, 16(39), 1-27.