AI-Driven Educational Reform: Enhancing Talent Cultivation in Computer-Related Majors for the Digital Era

Synthesis: Grounded in China's "new engineering" educational philosophy, this study designs and pilots an AI-integrated talent-cultivation reform for computer-related majors that embeds AI across curriculum, teaching, practice, and assessment. A pre/post cohort comparison across four undergraduate programs reports clear gains in programming completion, algorithmic reasoning, teamwork, and teaching effectiveness, though the single-institution, non-experimental design limits generalization.

Key Findings

• Measurable competency gains. Comparing the pre-reform cohort with the post-reform cohort across four undergraduate programs (Computer Science and Technology, Software Engineering, Network Engineering, and Data Science and Big Data Technology), the authors report consistent improvements on every measured dimension — programming project completion, algorithmic reasoning, and teamwork and project management. The headline gain was a programming completion rate roughly a fifth higher in the post-reform cohort, with algorithmic reasoning improving nearly as much, and statistical tests confirmed these differences were not due to chance.

• Personalized learning drove engagement. Students who actively used the AI-based Personalized Learning system showed fewer repetitive coding mistakes and more efficient learning trajectories, and average system-logged interactions per student rose markedly. Most surveyed students reported that the AI-driven diagnostic tools improved their conceptual understanding and confidence in tackling complex programming problems.

• Teaching effectiveness improved. Questionnaire scores rose overall, with instructional clarity, feedback timeliness, and student interaction all up; instructors used Learning Analytics dashboards to identify struggling students in real time and target interventions.

• Innovation and industry alignment rose. Student-led teams grew over two years, alongside more student research papers and patent applications. Partnerships with Huawei and Neusoft supported co-designed labs, dual-supervision mentorship, and co-constructed courses, strengthening employment outcomes in top-tier technology firms.

Study Design & Method

A program-wide reform initiative evaluated as a descriptive pre/post cohort comparison across two consecutive academic years. A total of 244 students participated in the post-reform cohort (against 205 in the pre-reform cohort) across four computer-related undergraduate programs, instructed by five faculty members, with data collected over two consecutive semesters. Because the reform was implemented program-wide, no parallel control group was available.

The intervention had five components: Curriculum Design restructuring built on a three-layer competency map (core competencies, sub-competencies, performance indicators); smart-teaching integration; interdisciplinary and collaborative talent cultivation via a dual-mentor system and cross-disciplinary course alliance; practice-oriented learning platforms spanning physical labs, virtual platforms, and enterprise and international collaboration; and a multi-dimensional evaluation system grounded in constructive alignment. AI was implemented through an intelligent dashboard (Python/Dash/Plotly with a PostgreSQL backend), adaptive feedback loops driven by a Reinforcement Learning model, and semantic-similarity novelty scoring using the Qwen-7B pre-trained Large Language Models (LLMs). Outcomes were analyzed with t-tests and effect sizes on programming completion, algorithmic reasoning, teamwork competence, and a Likert-based teaching-effectiveness survey.

What this means for practice

• Instructors. Route students into the AI-based Personalized Learning system rather than treating it as optional, and use the diagnostic dashboards to identify struggling students in real time and target interventions: students who actively used the system made fewer repetitive coding mistakes, moved through material more efficiently, and reported improved conceptual understanding and confidence on complex programming problems.
• Designers. Embed AI across curriculum, pedagogy, practice and assessment rather than running isolated course experiments, so that it supports differentiated trajectories and formative feedback instead of replacing teachers; log and feed back on student interactions, since average logged interactions per student rose markedly and the strongest measured gains were a 22.5% increase in programming project completion and an 18.2% gain in algorithmic reasoning.
• Designers. Use the reform's concrete, replicable components — AI-enhanced security and IoT courses, enterprise co-design and dual mentorship — as the template for computing programs aligning with industry demand.
• Administrators. Pair AI adoption with institutional and ethical infrastructure — AI Governance, transparent algorithmic grading and faculty Teacher AI Competency development — keeping teachers in oversight of AI evaluations under a Human AI Collaboration arrangement.
• Administrators. Back the reform with structured industry partnerships — co-designed labs, dual-supervision mentorship and co-constructed courses — which the authors link to stronger employment outcomes in top-tier technology firms.

Limitations

• Single-institution setting with specific corporate partners (Huawei and Neusoft), limiting generalizability; applicability to non-computer disciplines and other institutions remains unverified.

• Small sample (244 students) and short window (two consecutive semesters) preclude assessment of long-term effects or developmental trajectories.

• Program-wide implementation meant no parallel control group; conclusions rest on a descriptive pre/no-reform cohort comparison.

• Outcomes rely partly on self-report (student and faculty surveys) and context-specific assessment rubrics.

Connected Concepts

• CS Education · Higher Education · Curriculum Design · Learning Design · Personalized Learning
• Adaptive Learning · Learning Analytics · Formative Assessment · Collaborative Learning · Authentic Assessment · Human AI Collaboration · Teacher AI Competency

Connected Articles

• Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis — AI integration in a software-engineering curriculum, complementary to the curriculum-restructuring lens here.
• A meta-analysis of the effect of generative AI on productivity and learning in programming — meta-analytic evidence on generative-AI effects in programming education, relevant to this reform's AI-enhanced CS courses.
• Pedagogical Promise and Peril of AI: A Text Mining Analysis of ChatGPT Research Discussions in Programming Education — text-mining analysis of ChatGPT in programming education, overlapping this study's computing-context focus.
• Modeling AI-TPACK in Practice: Insights from Teachers'' Multi-Agent Workflow Design — teacher development for AI-integrated instruction, echoing the faculty-competency component of the reform.
• Integrating AI in Online Learning in Higher Education: A Literature Review — AI integration in higher-education online learning, consistent with the smart-teaching ecosystem described.

Citation

Wang, J., & Li, P. (2026). AI-Driven Educational Reform: Enhancing Talent Cultivation in Computer-Related Majors for the Digital Era. Frontiers in Psychology, 17, 1790916.