Navigating GenAI in assessment design with supercharging and proofing

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Assessment Design

Background:

• Generative AI (GenAI) is significantly impacting higher education, prompting a reevaluation of traditional teaching, learning, and assessment methods.
• The increasing integration of GenAI necessitates a critical examination of current assessment strategies and academic integrity policies.

Purpose of the Study:

• To explore differing perspectives on GenAI integration in higher education assessment.
• To introduce the AI-supercharging and AI-proofing (ASAP) framework for rethinking assessment design.
• To describe a faculty workshop designed to foster reflective discussions on GenAI, assessment, and academic integrity.

Main Methods:

• Development and implementation of a faculty-facing workshop utilizing the ASAP framework.
• Provision of facilitation resources and adaptable examples for broader use.
• Evaluation of workshop efficacy and faculty perspective shifts.
• Creation of a guided worksheet to support educators in assessment redesign.

Main Results:

• Workshops using the ASAP framework foster reflective conversations on assessment and academic integrity.
• Faculty participants demonstrated an increased willingness to adopt GenAI and developed a more positive, nuanced view of its classroom use.
• The ASAP framework supports educators in redesigning meaningful, well-aligned assessments that thoughtfully integrate GenAI.

Conclusions:

• Engaging with the ASAP framework empowers educators to thoughtfully integrate GenAI into assessments.
• The framework aids in overcoming hesitancy towards AI adoption and promotes the redesign of more effective and aligned assessments.
• Facilitating discussions around GenAI and assessment can lead to positive shifts in faculty perspectives and practices within teaching and learning communities.