Preparing students for an AI-driven world: generative AI and curriculum reform in higher education
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Intersections among GenAI, higher education, and curriculum reform. Intersections represent areas where these domains collaborate to address challenges and opportunities in modern education.
view moreCredit: Ying Ma, Youxiang Su, Mingda Li, Yu Zhang, Wantong Chai, Amin Huang, Xiaofei Zhao.
This research article addresses the imperative of curriculum reform in higher education to prepare students for an AI-driven world amid the rapid advancement of Generative AI (GenAI). It proposes a comprehensive framework centered on three core strategies: fostering interdisciplinary AI literacy via tiered courses (foundational concepts, applied uses, advanced techniques) covering technical fundamentals, ethical implications, and practical tool use; shifting pedagogy from rote memorization to problem-solving through active learning (e.g., problem-based learning, project-based learning) and interdisciplinary collaboration; and establishing dynamic curriculum update mechanisms (industry partnerships, modular design, self-directed learning cultivation).
Additionally, the study examines critical implementation considerations, including faculty training, resource allocation, ethical issues (bias, privacy, academic integrity), assessment redesign (prioritizing higher-order thinking), and strategies to preserve academic honesty. It concludes with future research directions and emphasizes institutions’ urgent need to adopt proactive, ethical, and adaptive measures to harness GenAI’s potential for equitable, effective education.
The work titled “Preparing Students for an AI-Driven World: Generative AI and Curriculum Reform in Higher Education”, was published on Frontiers of Digital Education (published on September 15, 2025).
Journal
Frontiers of Digital Education
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Preparing Students for an AI-Driven World: Generative AI and Curriculum Reform in Higher Education
Towards AI: the evolution of digital education policy in the United Kingdom
Higher Education Press
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Phased release frequency of digital education policies in the UK.
view moreCredit: Jialong Xu, Mengyu Luo, Xinxin Zhang
This research article examines the evolution of the UK’s digital education policies from 2008 to 2024 through discourse analysis of 21 official policy documents and Foucault’s power discourse theory. It divides the evolution into four stages: 1) 2008–2012 (early exploration and slow progress, focusing on foundational infrastructure and basic policies); 2) 2013–2014 (policy acceleration and global engagement, addressing the digital divide and enhancing international cooperation); 3) 2015–2019 (diversification and systemic integration, emphasizing digital skills cultivation for emerging industries and equity); and 4) 2021–2024 (AI-driven transformation, prioritizing AI integration, data security, and ethical use).
Key policy trends identified include comprehensive digital technology integration, personalized education for digital natives, digital literacy cultivation and teacher development, bridging the digital divide, and strengthening research-policy links. The study highlights the UK’s shift from basic digital initiatives to AI-focused education, offering insights for global policymakers on balancing technological innovation, educational equity, and ethical considerations, with practical validation from cases like the “Sabrewing Programme.”
The work titled “Towards AI: The Evolution of Digital Education Policy in the United Kingdom”, was published on Frontiers of Digital Education (published on September 15, 2025).
Journal
Frontiers of Digital Education
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Towards AI: The Evolution of Digital Education Policy in the United Kingdom
Explainable few-shot knowledge tracing
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The cognition-guided framework for explainable few-shot knowledge tracing
view moreCredit: Haoxuan Li, Jifan Yu, Yuanxin Ouyang, Zhuang Liu, Wenge Rong, Huiqin Liu, Juanzi Li, Zhang Xiong
Conventional knowledge-tracing models require thousands of question–answer pairs and offer little pedagogical insight, a mismatch to real classrooms where teachers rely on sparse evidence and explicit reasoning to guide intervention. Leveraging the few-shot in-context learning capability of GPT-4 and GLM-4, the proposed “observation–cognition–interpretation” pipeline first selects a small set of representative attempts, then fuses item text and skill tags to infer mastery, and finally articulates weaknesses and remedial suggestions in plain language. Experiments on FrcSub, MOOCRadar, and XES3G5M show that with only 4–16 samples per learner the approach matches or surpasses deep baselines such as DKT, AKT, and SAINT, while expert raters deem its explanations substantially credible. By coupling accurate prediction with actionable feedback under extreme data constraints, the study opens a practical path toward small-sample, strongly interpretable learning analytics and lays groundwork for extending assessment to open-ended problems and multimodal coursework.
The work titled “Explainable Few-shot Knowledge Tracing”, was published on Frontiers of Digital Education (published on September 22, 2025).
Journal
Frontiers of Digital Education
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Explainable Few-Shot Knowledge Tracing
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