Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

Abstract

In recent years, many colleges and universities have been experimenting and exploring the evaluation of education and teaching system and have achieved certain results. In order to understand the quality of education and teaching system in colleges and universities, to improve the school conditions, and to promote the reform of teaching management, methods and means of evaluating the quality of education and teaching system in general higher education institutions are needed. Modern university education and teaching system should realize the combination of classroom teaching and practice teaching, and education and teaching system adopts the mode of the combination of on-campus practice and off-campus practice, so the design of teaching system is the key to the quality of teaching. Aiming at the current problem that talents cultivated by colleges and universities can hardly meet social demands in terms of engineering practice ability, innovation ability, and international competitiveness, this paper proposes the evaluation and adjustment of college education and teaching system driven by algorithms based on artificial intelligence (AI). By designing the teaching system of talent cultivation, and then establishing a quantitative and controllable quality assurance system for practical teaching, a new mechanism for the design of university education system is further explored. Specifically, the framework of the instructional system is built with the aid of an actor-critic algorithm in reinforcement learning, which assists in the design of the university education system, allowing students to truly understand, master and flex their knowledge, and strengthening the correct understanding of the students’ internal learning mechanisms. The practical teaching effect shows that the AI-driven instructional designs are more popular with contemporary students and have higher evaluation scores. The numerical experiment results also show the stability of the instructional design, overcoming the drawbacks of traditional manual subjectivity in the design. AI-driven college education and teaching system is conducive to cultivating students’ solid technical theoretical foundation. Therefore, through the AI-driven teaching system to strengthen the training of practical ability, so as to comprehensively improve students' comprehensive quality and innovation ability.

Artificial intelligence in higher education: the state of the field

“…AI is also used for other tasks, such as organizing the sequence of curriculum in pacing guides for future groups of students and also designing instruction. Zhang (2022) described how designing an AI teaching system of talent cultivation and using the digital affordances to establish a quality assurance system for practical teaching, provides new mechanisms for the design of university education systems. In developing such a system, Zhang found that the stability of the instructional design, overcame the drawbacks of traditional manual subjectivity in the instructional design.…”

The Role of Artificial Intelligence Applications in Enhancing the Quality of Online Higher Education

“…A study conducted by (Zhang, 2022) aims to propose and evaluate an AI-driven approach for the evaluation and adjustment of online higher education and teaching systems. The aim is to address the current challenges faced by colleges and universities in meeting the demands of society in terms of engineering practice ability, innovation ability, and international competitiveness.…”

A Systematic Review of Generative AI and (English Medium Instruction) Higher Education

“…Again addressing RQ1, the multiple affordances of GenAI-facilitated pedagogical innovation in HE are documented in a number of publications, such as Archibald et al ( 2023), Chang et al (2022), Cox et al (2022), Razia et al (2022), Zhang (2022), andZhou (2022), which detail advantages such as content generation for lectures or assessments, assisted data analysis, and greater administrative efficiency. Ghnemat et al (2022) take a similar approach and make a convincing call for further investigation into AI-based learning.…”