Construction and Practice of an AI General Education Teaching Model for Liberal Arts and Management Disciplines under the “AI+HI” Dual-Engine Drive

DOI: https://doi.org/10.62517/jnse.202617505

Author(s)

Lei Wang*, Zhihao Li, Jianing Wei

Affiliation(s)

School of Business, Guilin University of Electronic Technology, Guilin, Guangxi, China *Corresponding Author.

Abstract

The swift advancement of generative artificial intelligence (AI) is bringing fundamental changes to how knowledge is produced and to the delivery of general education at colleges and universities. Nevertheless, AI general education courses designed for students in liberal arts and management programs continue to encounter a range of entrenched problems: learners’ comprehension of AI capability boundaries tends to remain at a surface level, the transfer of disciplinary knowledge into professional settings is frequently inadequate, AI-produced content poses threats to the credibility of formative assessment, and human–AI collaborative thinking receives insufficient attention in training. In light of these concerns, the present study, built upon the Outcome-Based Education (OBE) framework and theories of human–AI collaborative learning, puts forward a dual-engine teaching model driven by “AI+HI” (Artificial Intelligence + Human Intelligence). Through the guiding principle of “machine computation, human deliberation,” the model articulates the educational boundary of human–machine collaboration and creates a twin-track mechanism in which AI boosts efficiency and expands scale, whereas HI fosters value formation and elevates instructional quality. The model also outlines a four-dimensional implementation route covering cognitive reconstruction, scenario-based empowerment, process-based evidence generation, and thinking development, and it reshapes the interaction mechanism among teachers, students, machines, and the environment while instituting a three-tier verification-based evaluation framework that combines AI preliminary screening, instructor review, and randomized oral defense. Anchored in the Chaoxing learning platform and industry–education integration bases, the model further builds an organized resource system composed of discipline-specific scenario case libraries and prompt template repositories. Ultimately, the study endeavors to close the gap between technological instrumental rationality and humanistic value orientation in large-scale general education classrooms, thereby offering a transferable theoretical framework and a practical route for developing and pedagogically transforming AI-enabled general education courses in liberal arts and management disciplines.

Keywords

Artificial Intelligence General Education; Liberal Arts and Management Disciplines; AI+HI; Human–AI Collaboration; Teaching Model

References

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