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Rajasekar, A., Mallenahalli, S., Mirza, I., Palaboyina, P. K., Pola, S. K., Quadri, S. F. et al. (2024). AI Framework for Scalable Automated Continuous Formative Assessment. In 2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 1-6). IEEE.
https://doi.org/10.1109/tale62452.2024.10834316

has been cited by the following article:

• TITLE: Research on the Through-Integrated Teaching Evaluation Model in Higher Education in the AI Era

  AUTHORS: Miaomiao Yang, Bin Li, Xiaoli Wang, Jingming Wang, Qian Sun, Xuemei Zhang

  KEYWORDS: Teaching Evaluation Model, Through-Integrated, AI-Empowered, Teaching Reform

  JOURNAL NAME: Creative Education, Vol.17 No.11, November 10, 2026

  ABSTRACT: This study investigates the through-integrated teaching evaluation model in higher education in the era of artificial intelligence. We propose an AI-empowered, through-integrated evaluation model that places students’ learning outcomes at the core of assessment. Built on an architecture of “vertical continuity and horizontal integration”, the model integrates multiple dimensions—students, learning, and institutions—and employs an “multi-source and multi-faceted” approach to strengthen process evaluation, explore value-added evaluation, and improve comprehensive evaluation. The proposed model offers a conceptual foundation for enhancing teaching quality, improving evaluation objectivity, and fostering lifelong learning habits. This study establishes a theoretical framework for AI-empowered through-integrated evaluation.