Scaffolded Learning and Assessment In The Age of Ai: Designing Authentic Learning Experiences

Scaffolding is a metaphor that describes the temporary, future-oriented, and targeted support that enables students to progress from their current level of understanding to where they need to be to achieve the desired learning outcomes. Rooted in Vygotsky’s concept of the Zone of Proximal Development (ZPD), scaffolding provides structured assistance that helps students learn within the space just beyond their independent capability. In the age of AI, a scaffolding approach to assessment helps solve two major challenges in higher education: maintaining authenticity and integrity in student work, and ensuring deep learning and critical skill development rather than superficial output generation using AI tools. This session introduces participants to a scaffolded learning and assessment approach implemented in Business Simulation 350, a core capstone course for third-year undergraduate students at the University of Auckland (targeting 250 to 450 students per semester). The course integrates multiple formative, summative, individual, and group assessments, including a video presentation, reflective writing, simulation activities, in-class oral assessments, a comprehensive group board report, and an end-of-semester interview. Approximately five distinct types of assessments are distributed across several checkpoints throughout the semester, providing continuous opportunities for feedback and reflection. Each assessment is designed to encourage the responsible use of AI, allowing students to demonstrate what they have learned, identify areas for improvement, and engage in deeper cognitive processes. The scaffolded structure supports students in developing critical thinking, collaboration, and higher-order decision-making skills while fostering transparency and ethical digital literacy among learners. During this session, participants will first explore how scaffolded assessment supports authentic and ethical learning in the age of AI. After that, they will engage in designing scaffolded assessment structures for their own courses, where the focus will not be on AI detection or restriction, but on creating learning opportunities that encourage students to demonstrate how they think, reflect, and apply knowledge. The session will equip educators with practical frameworks to assess student learning authentically, reflectively, and transparently in AI-enabled learning environments.