Analysis of Teaching Mode Innovation and Learning Effectiveness Assisted by Artificial Intelligence

Abstract In the context of artificial intelligence-assisted teaching, this paper constructs a deep knowledge tracking model (DKT-FC) that incorporates the Ebbinghaus forgetting curve. By analyzing students’ learning data and combining the theory of forgetting curve, it tracks students’ mastery of knowledge and forgetting more accurately. Afterwards, an intelligent teaching and learning support system was designed on this basis, and an innovative STSE-based teaching model that integrates science, technology, social and environmental education into the teaching process was constructed. Finally, the learning effectiveness under this model was clarified by covariance analysis and correlation analysis. The results showed that among the students in the experimental group, the correlation between the total score of co-ordination and co-ordination elaboration was the largest, with a correlation coefficient as high as 0.8579, and all the indicators showed a significant positive correlation with each other (P < 0.05). In the control group, although the students’ reading unification was positively correlated with the dimensions, the correlation coefficient was significantly lower than that of the experimental group. And the difference between students’ integrated questioning and integrated summary (0.0614) and integrated elaboration (0.14) in the control group is not significant (P > 0.05). The experimental results proved that the innovative teaching model of intelligent tutoring based on DKT-FC proposed in this paper has a good application effect in monitoring students’ learning effectiveness.