AI-Based Assessment Systems and Their Effect on Biology Academic Performance

Abstract

Introduction of Artificial Intelligence (AI) into the educational assessment system is changing the teaching and learning process, in particular sciences. The study focuses on the impacts of AI assessment tools on the quality of feedback, motivation, engagement, and conceptual understanding of biology learners based on the constructivist and data[1]driven learning theories. The mixed-method design allowed the research to apply both quantitative and qualitative approaches to the study to provide a holistic description of the impact of AI on learning outcomes. Quantitative data were collected in a sample population of 320 biology students in the secondary level by using achievement scores on pre-test and post-test and perceptions were collected with the help of a structured Likert-scale questionnaire. Qualitative information on the topic of using AI assessment systems was gathered, and it was represented by semi[1]structured interviews with biology teachers, who utilized the following AI assessment systems: Grade scope, Century Tech, and Google Classroom AI analytics. Data was analyzed using descriptive statistics, t-tests, ANOVA, and multiple regression analysis to determine the connection between AI assessment and performance indicators. The results revealed a statistically significant improvement in the academic health and motivation of the students after exposure to AI-based formative assessment. It is suggested to introduce AI evaluation tools into the curriculum design, educate teachers on AI analytics, and render the use of AI ethical and fair.