A proposed framework for achieving higher levels of outcome-based learning using generative AI in education

Shakib Sadat Shanto; Zishan Ahmed; Akinul Islam Jony

doi:10.55056/etq.788

Authors

Shakib Sadat Shanto American International University-Bangladesh https://orcid.org/0009-0009-8798-9010
Zishan Ahmed American International University-Bangladesh https://orcid.org/0009-0004-9598-917X
Akinul Islam Jony Open University of Catalonia https://orcid.org/0000-0002-2942-6780

DOI:

https://doi.org/10.55056/etq.788

Keywords:

generative AI, Bloom's taxonomy, outcome-based learning, higher-order thinking, higher education

Abstract

Generative artificial intelligence (GAI) systems like ChatGPT have gained popularity due to their ability to generate human-like text. Educators are exploring how to leverage these systems to facilitate and promote learning and develop skills and abilities. This study proposes a conceptual framework aimed at facilitating outcome-based learning through the utilization of GAI tools. The overall aim of this study is to provide a way for integrating GAI to support outcome-based education paradigms focused on learning objectives by aiding cognitive ability development according to Bloom's taxonomy. This paper introduces a framework called the ACE Framework (AI-Enhanced Cognition for Outcome-Based Learning), which organizes the integration of emerging large language models to facilitate advanced analysis, synthesis, and evaluation, as defined by Bloom's taxonomy, from basic knowledge recall to complex conceptualization. To empirically assess the effectiveness of unaided and GAI-assisted approaches, an analysis of real-world scenarios was conducted, where 20 college students created open-ended written solutions. For every response set, human raters classified shown cognitive abilities into Bloom's levels. In structured GAI integration exercises, participants learnt about problems using models such as GPT-4 and framed analytical answers. Comparative benchmarking reveals significant enhancements in average ratings from predominant comprehension (3.35) to top-tier synthesis (4.85) after AI scaffolding based on the methodology. With six students reaching the highest evaluation tier, guided AI interactions showcase excellent ability to promote outcome-based learning. Despite limitations in sample size and assessment techniques requiring further investigation, results align with priorities of outcome-driven education models prioritizing higher-order cognition -- substantiating structured AI incorporation potential.

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