Toward Asset-based Instruction and Assessment in Artificial Intelligence in Education

The artificial intelligence in education (AIED) community has produced technologies that are widely used to support learning, teaching, assessment, and administration. This work has successfully enhanced test scores, course grades, skill acquisition, comprehension, engagement, and related outcomes. However, the prevailing approach to adaptive and personalized learning has two main steps. First, the process involves detecting the areas of knowledge and competencies where students are deficient. This process also identifies when or how a student is considered “at risk” or in some way “lacking.” Second, the approach involves providing timely, individualized assistance to address these deficiencies. However, a considerable body of research outside our field has established that such deficit framing, by itself, leads to reactive and less productive strategies. In deficit-based frameworks, powerful student strengths, skills, and schemas—their assets—are not explicitly leveraged. In this paper, we outline an asset-based paradigm for AIED research and development, proposing principles for our community to build upon learners’ rich funds of knowledge. We propose that embracing asset-based approaches will empower the AIED community (e.g., educators, developers, and researchers) to reach broader populations of learners. We discuss the potentially transformative role this approach could play in supporting learning and personal development for all learners, particularly for students who are historically underserved, marginalized, and “deficit-ized.”