Go to a small liberal-arts college if you can. I n the waning heat of last summer, freshly back in my office at a major research university, I found myself considering the higher-education hellscape that had lately descended upon the nation. I'd spent months reporting on the Trump administration's attacks on universities for, speaking with dozens of administrators, faculty, and students about the billions of dollars in cuts to public funding for research and the resulting collapse of " college life ."At Initially, I surveyed the situation from the safe distance of a journalist who happens to also be a career professor and university administrator. I saw myself as an envoy between America's college campuses and its citizens, telling the stories of the people whose lives had been shattered by these transformations. By the summer, though, that safe distance had collapsed back on me.

Academic advising is critical to student success in higher education, yet high student-to-advisor ratios limit advisors' capacity to provide timely support, particularly during peak periods. Recent advances in Large Language Models (LLMs) present opportunities to enhance the advising process. We present AdvisingWise, a multi-agent system that automates time-consuming tasks, such as information retrieval and response drafting, while preserving human oversight. AdvisingWise leverages authoritative institutional resources and adaptively prompts students about their academic backgrounds to generate reliable, personalized responses. All system responses undergo human advisor validation before delivery to students. We evaluate AdvisingWise through a mixed-methods approach: (1) expert evaluation on responses of 20 sample queries, (2) LLM-as-a-judge evaluation of the information retrieval strategy, and (3) a user study with 8 academic advisors to assess the system's practical utility. Our evaluation shows that AdvisingWise produces accurate, personalized responses. Advisors reported increasingly positive perceptions after using AdvisingWise, as their initial concerns about reliability and personalization diminished. We conclude by discussing the implications of human-AI synergy on the practice of academic advising.

Identifying the determinants of academic success in basic education represents a central challenge for educational research and policymaking, particularly in a country with Brazil's vast dimensions and socioeconomic heterogeneity (Issah et al. 2023). A systemic approach is crucial, as student performance is influenced by a complex interplay of factors spanning individual, academic, socioeconomic, and institutional domains (Barrag an Moreno and Guzm an Rinc on 2025). The System of Assessment of Basic Education (SAEB), conducted by the National Institute for Educational Studies and Research An ısio Teixeira (INEP) (INEP 2025), provides a rich, multi-level dataset uniquely suited for such an analysis (Bonamino et al. 2010). The public availability of its anonymized microdata enables the research community to investigate the intricate relationships between student proficiency and a wide array of contextual factors, from socioeconomic backgrounds to school infrastructure and teacher profiles. Consequently, the SAEB microdata is an essential resource for data-driven research aimed at informing and evaluating educational policies in the country (Lundberg and Lee 2017b; Mazoni and Oliveira 2023). While traditional statistical methods are common, the Educational Data Mining (EDM) paradigm offers powerful tools for uncovering complex, non-linear patterns from such data (Romero and Ventura 2010). Furthermore, we demonstrate that by interpreting the model's classification results with XAI techniques, our method provides data-driven insights for educators and policymakers (Idrizi 2024). The primary objective of this research is thus to develop and evaluate a multi-level machine learning model to identify the key systemic factors associated with the academic performance of 9th-grade and high school students, using the SAEB microdata. Building upon this perspective, the study shifts its analytical focus from purely individual student interventions toward addressing the systemic determinants that shape educational outcomes in Brazilian basic education.

Research on how the popularization of generative Artificial Intelligence (AI) tools impacts learning environments has led to hesitancy among educators to teach these tools in classrooms, creating two observed disconnects. Generative AI competency is increasingly valued in industry but not in higher education, and students are experimenting with generative AI without formal guidance. The authors argue students across fields must be taught to responsibly and expertly harness the potential of AI tools to ensure job market readiness and positive outcomes. Computer Science trajectories are particularly impacted, and while consistently top ranked U.S. Computer Science departments teach the mechanisms and frameworks underlying AI, few appear to offer courses on applications for existing generative AI tools. A course was developed at a private research university to teach undergraduate and graduate Computer Science students applications for generative AI tools in software development. Two mixed method surveys indicated students overwhelmingly found the course valuable and effective. Co-authored by the instructor and one of the graduate students, this paper explores the context, implementation, and impact of the course through data analysis and reflections from both perspectives. It additionally offers recommendations for replication in and beyond Computer Science departments. This is the extended version of this paper to include technical appendices.

The equitable assessment of individual contribution in teams remains a persistent challenge, where conflict and disparity in workload can result in unfair performance evaluation, often requiring manual intervention - a costly and challenging process. We survey existing tool features and identify a gap in conflict resolution methods and AI integration. To address this, we propose a framework and implementation design for a novel AI-enhanced tool that assists in dispute investigation. The framework organises heterogeneous artefacts - submissions (code, text, media), communications (chat, email), coordination records (meeting logs, tasks), peer assessments, and contextual information - into three dimensions with nine benchmarks: Contribution, Interaction, and Role. Objective measures are normalised, aggregated per dimension, and paired with inequality measures (Gini index) to surface conflict markers. A Large Language Model (LLM) architecture performs validated and contextual analysis over these measures to generate interpretable and transparent advisory judgments. We argue for feasibility under current statutory and institutional policy, and outline practical analytics (sentimental, task fidelity, word/line count, etc.), bias safeguards, limitations, and practical challenges.

As generative AI diffuses through academia, policy-practice divergence becomes consequential, creating demand for auditable indicators of alignment. This study prototypes a ten-item, indirect-elicitation instrument embedded in a structured interpretive framework to surface voids between institutional rules and practitioner AI use. The framework extracts empirical and epistemic signals from academics, yielding three filtered indicators of such voids: (1) AI-integrated assessment capacity (proxy) - within a three-signal screen (AI skill, perceived teaching benefit, detection confidence), the share who would fully allow AI in exams; (2) sector-level necessity (proxy) - among high output control users who still credit AI with high contribution, the proportion who judge AI capable of challenging established disciplines; and (3) ontological stance - among respondents who judge AI different in kind from prior tools, report practice change, and pass a metacognition gate, the split between material and immaterial views as an ontological map aligning procurement claims with evidence classes.

This chapter introduces the AI & Data Acumen Learning Outcomes Framework, a comprehensive tool designed to guide the integration of AI literacy across higher education. Developed through a collaborative process, the framework defines key AI and data-related competencies across four proficiency levels and seven knowledge dimensions. It provides a structured approach for educators to scaffold student learning in AI, balancing technical skills with ethical considerations and sociocultural awareness. The chapter outlines the framework's development process, its structure, and practical strategies for implementation in curriculum design, learning activities, and assessment. We address challenges in implementation and future directions for AI education. By offering a roadmap for developing students' holistic AI literacy, this framework prepares learners to leverage generative AI capabilities in both academic and professional contexts.

Feedback is one of the most powerful influences on student learning, with extensive research examining how best to implement it in educational settings. Increasingly, feedback is being generated by artificial intelligence (AI), offering scalable and adaptive responses. Two widely studied approaches are directive feedback, which gives explicit explanations and reduces cognitive load to speed up learning, and metacognitive feedback which prompts learners to reflect, track their progress, and develop self-regulated learning (SRL) skills. While both approaches have clear theoretical advantages, their comparative effects on engagement, confidence, and quality of work remain underexplored. This study presents a semester-long randomised controlled trial with 329 students in an introductory design and programming course using an adaptive educational platform. Participants were assigned to receive directive, metacognitive, or hybrid AI-generated feedback that blended elements of both directive and metacognitive feedback. Results showed that revision behaviour differed across feedback conditions, with Hybrid prompting the most revisions compared to Directive and Metacognitive. Confidence ratings were uniformly high, and resource quality outcomes were comparable across conditions. These findings highlight the promise of AI in delivering feedback that balances clarity with reflection. Hybrid approaches, in particular, show potential to combine actionable guidance for immediate improvement with opportunities for self-reflection and metacognitive growth.

As the global reach of online higher education continues to grow, universities are increasingly accommodating students from diverse cultural backgrounds (Tereshko et al., 2024). This can present a number of challenges including linguistic barriers (Ullah et al., 2021), cultural differences in learning style (Omidvar & Tan, 2012), cultural sensitivity in course design (Nguyen, 2022) and perceived isolation when students feel their perspectives or experiences are not reflected or valued in the learning environment (Hansen-Brown et al., 2022). Ensuring active engagement and reasonable learning outcomes in such a environments requires distance educational systems that are not only adaptive but also culturally resonant (Dalle et al., 2024). Both embodied and virtual AI-Agents have great potential in this regard as they can facilitate personalized learning and adapt their interactions and content delivery to align with students' cultural context. In addition, Generative AI (GAI), such as, Large Language Models (LLMs) can amplify the potential for these culturally aware AI agents to address educational challenges due to their advanced capacity for understanding and generating contextually relevant content (Wang et al., 2024). This chapter reviews existing research and suggests the usage of culturally aware AI-Agents, powered by GAI, to foster engagement and improve learning outcomes in culturally diverse online higher education environments.

Computational Thinking (CT) is a key skill set for students in higher education to thrive and adapt to an increasingly technology-driven future and workplace. While research on CT education has gained remarkable momentum in K12 over the past decade, it has remained under-explored in higher education, leaving higher education teachers with an insufficient overview, knowledge, and support regarding CT education. The proliferation and adoption of artificial intelligence (AI) by educational institutions have demonstrated promising potential to support instructional activities across many disciplines, including CT education. However, a comprehensive overview outlining the various aspects of integrating AI in CT education in higher education is lacking. To mitigate this gap, we conducted this systematic literature review study. The focus of our study is to identify initiatives applying AI in CT education within higher education and to explore various educational aspects of these initiatives, including the benefits and challenges of AI in CT education, instructional strategies employed, CT components covered, and AI techniques and models utilized. This study provides practical and scientific contributions to the CT education community, including an inventory of AI-based initiatives for CT education useful to educators, an overview of various aspects of integrating AI into CT education such as its benefits and challenges (e.g., AI potential to reshape CT education versus its potential to diminish students creativity) and insights into new and expanded perspectives on CT in light of AI (e.g., the decoding approach alongside the coding approach to CT).