Abstract:

Assessment has long shaped teaching and learning in higher education. What and how instructors choose to assess learning signals which knowledge, reasoning, and skills are most valued, influencing both student learning and course design. In this paper, we propose a set of design and implementation principles for Scenario-Based Learning and Assessment (SBLA) that articulate what constitutes an SBLA and why these principles are especially important in AI-mediated learning and assessment environments. These principles clarify the defining features of SBLAs and provide a framework to guide their development and implementation across disciplines in higher education.

SBLAs were developed as a response to long-standing tensions in assessment practice, namely that assessments often measure something different from what instructors value. Traditional formats, such as multiple-choice items, prioritize measurement precision and content recall rather than deeper thinking, such as collaborative problem solving. In the age of AI, knowledge-focused items are easier to invalidate because students can simply query AI for answers instead of constructing solutions. As generative AI tools capable of producing text, solving problems, and generating explanations become widely accessible, they challenge traditional assessment formats and prompt renewed scrutiny of assessment practices. Because assessment shapes instructional priorities, AI’s emergence requires educators to reconsider not only how learning is assessed but also the kinds of learning experiences instruction should support.

In response to this context, some educators are exploring approaches that foreground complex thinking, contextual judgment, and authentic problem solving—capacities that extend beyond routine knowledge reproduction. SBLA represents one such approach. By situating learners within realistic, context-rich situations that require application of knowledge, analysis, and critical reasoning, SBLAs can provide insight into how students apply knowledge and navigate uncertainty in ways that more closely resemble real-world practice.

SBLA is not a new approach to assessment, but the rise of AI has provided an opportunity to expand its possibilities in higher education. Rather than relying on standardized assessments that may not fit a particular learning environment precisely, instructors can now leverage AI to create SBLAs tailored to their specific learning environments and objectives, including interactive elements that more closely resemble real-world contexts. In addition, AI can support not only SBLA construction but also the provision of impactful real-time feedback to learners.

Derived from ongoing and existing research on SBLAs, the principles presented in this paper will help to clarify the defining features of SBLAs, thereby contributing a framework for future designs that keep humans meaningfully in the loop while supporting ethical and productive engagement with AI. These principles, which are built from proven, student-centered instructional practice, can guide SBLA development and implementation across disciplines in higher education. They also provide a framework for understanding how, under what circumstances, and for whom SBLAs function in various learning contexts.

Keywords:

Scenario-based learning and assessment, artificial intelligence, design principles, higher education.