Abstract E-assessment technologies have rapidly evolved in higher education, transforming the evaluation of learning outcomes and the delivery of feedback to students and educators. This paper outlines the technological progression of assessment methods, from early computer-assisted systems to modern adaptive approaches powered by artificial intelligence. The main contributions of this paper are an analysis of technology drivers, current capabilities and challenges of e-assessment, as well as a structured long-term roadmap for future research directions. Based on a systematic analysis of the assessment process, key developments are identified, including advances in automatic item generation, flexible learner interaction formats, scalable feedback techniques, and personalized, adaptive assessment. At the same time, challenges remain in balancing adaptivity with data privacy, supporting diverse and authentic artifacts, and designing feedback that is both pedagogically meaningful and technically feasible. Socio-technical aspects such as trust and cultural factors add further complexity to system design. Recent progress in generative AI offers new opportunities for automation – especially in item creation and adaptive feedback – but also raises concerns regarding reliability and explainability. Finally, the article provides a forward-looking perspective on future directions and potential developments in the examined subfields of e-assessment over the next 10, 25, and 50 years.
Striewe et al. (Thu,) studied this question.