Developing Higher Education – Post-Pandemic – Influenced by AI

The impact of the recent pandemic on Higher Education has been widely discussed in terms of the effects on students and the loss of the normal educational experience, accompanied by discussions of how to return to normality. However, in this paper the authors challenge the reality of that previous so-called normality and propose a number of changes to academic practices to take advantage of the opportunities offered by this break in existing practice. Additionally, new developments in AI technology, particularly in chatbots, present significant challenges to existing assessment practices, which were already under challenge from existing research. This also presents significant opportunities to introduce new, academically focused and effective assessment practices and instruments, taken from existing research, to improve the quality of evaluation of student learning in higher education. So, this paper has two main considerations: the development of better student engagement models to enhance cohort effects in the student experience; and the introduction of improved assessment practices and instruments from existing research, in particular focusing on student ownership of evaluation models. It discusses a number of existing research outputs, several of them produced by the authors, and some current initiatives, and looks at the pros and cons of each, in terms of effectiveness and staff and student responses. It has been widely reported that the student experience across a wide range of subjects, and in the whole range of higher education institutions, has been very badly affected by lockdowns and other changes brought about by the pandemic. In the engineering/computing community the authors have experienced first-hand the changes occasioned during this period, and have seen students become isolated, disaffected, anti-social and, in many cases, disengaged from their studies. It can be argued that one key criterion of the student experience that online delivery of learning does not support well is cohort-formation, and that developing entry-level cohort-based learning experiences, both online and face-to-face, offers a route more effective student engagement and retention. The authors discuss a number of such initiatives reported in existing research, and also describe a new initiative being developed at their own institution, based on early immersion in advanced technologies to spark interest and creativity, and thence engagement, in entry-level technology students. With regard to assessment and evaluation practices, there is a huge body of existing research questioning the widely used examination and coursework processes, arguing that these are only retained for administrative and cost efficiencies, not for any academic benefit. The recent release of ChatGPT by OpenAI has added considerable flame to the fire of essay-mills, code-farms, and bespoke thesis-writing, which has supported students cheating in assessment processes for many years. The authors take this opportunity to reconsider assessment practices, in the light of many successful models reported in the existing research, and to develop a new model of assessment, where students take responsibility for the ownership and evaluation of their own learning, mediated by academic processes.