Enhancing ABET Summative Direct Assessment with AI and Blockchain: A Framework for Personalized Learning and Secure Evaluation - Qutaiba I. Ali (International Journal of Algorithms Design and Analysis Review, 2024)
Accreditation Board for Engineering and Technology (ABET) emphasizes the achievement of specific measurable learning outcomes. However, conventional assessment methods often find it challenging to accurately capture the complexities of student learning and program effectiveness within the ABET framework. This study proposes a novel framework that enhances ABET summative direct assessment by integrating a carefully structured, weighted assessment system with the transformative potential of artificial intelligence (AI) and blockchain technologies. The framework leverages AI to personalize learning pathways, automate feedback generation, and drive data-driven curriculum improvement, while blockchain technology ensures secure data management, transparent grade recording, and verifiable student credentials. By grounding this integration in sound pedagogical principles and addressing ethical considerations, the proposed framework aims to create a more efficient, trustworthy, and learner-centric assessment experience that empowers both students and educators to achieve better learning outcomes. This study also describes the roadmap of the integration of AI and blockchain technologies with the ABET assessment framework which involves the examination of the practicality, efficiency, and impact of these technologies at different stages of the assessment process. Another contribution is to identify and recommend the most suitable AI and blockchain tools for effective implementation in ABET assessment. This involves a comparative analysis of available technologies, considering factors such as compatibility with ABET principles, data security, and the potential to enhance transparency, efficiency, and personalization in student assessments. We aim to reinforce the proposed approach by harnessing modern technologies in AI and advanced security measures to enhance the effectiveness, efficiency, and security of our proposed assessment framework.
Keywords: ABET accreditation; AI; Large language model (LLMs); Student outcome assessment; assessment methodology; blockchain; educational quality