W. Danang Arengga, Siti Sendari, Heru Wahyu Herwanto, Mukaromah, Aan Anjar Setyowati, Samsul Setumin
The integration of generative artificial intelligence (AI) into educational assessment has shown potential in addressing inefficiencies in traditional evaluation methods, particularly in time-constrained STEM classrooms. This study proposes a hybrid framework that synergizes teacher expertise with generative AI to streamline the evaluation of verbal skills-a critical yet underexplored competency in STEM education. By focusing on collaborative dialogue, problemsolving explanations, and conceptual reasoning, this research aims to develop a system that enhances assessment efficiency while preserving the nuanced judgment of educators. Verbal skill evaluation in STEM contexts-such as assessing students' ability to articulate hypotheses, defend solutions, or collaborate in technical discussions-remains labor-intensive and subjective. Teachers spend significant time analyzing spoken or written responses, often sacrificing opportunities for personalized instruction. While generative AI model proficiency in language processing standalone using in education raises concerns: (1) lack of contextual awareness in STEM-specific discourse, (2) potential biases in automated scoring, and (3) displacement of teachers' formative feedback roles. In this research using a mixed-methods approach was employed across three phases: Framework Development, Pilot Testing, Scalability. By bridging the divide between automation and human judgment, this hybrid framework demonstrates that generative AI need not replace teachers but can instead amplify their capacity to nurture critical verbal skills in STEM. Future work explored by adaptive AI tutoring systems that leverage this model to provide real-time dialogue support during student presentations or group discussions. © 2025 IEEE.
State University of Malang, Department of Electrical and Informatics Engineering, Malang, Indonesia
