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This book offers a bold and comprehensive vision for creating, leading, and sustaining experiential learning (EL) ecosystems on college campuses. Drawing on extensive interviews with EL experts and collaboratively written by eight seasoned professionals, it redefines EL not just as a learning method but as a dynamic ecosystem of people, practices, policies, and institutional structures. This crucial shift in perspective will empower educators and administrators to embrace the diversity of EL and leverage its strengths through cross-campus collaboration. Readers will find practical tools and actionable strategies to deepen their EL objectives, connect with allies across campus, and build a shared vision for the future of EL in U.S. higher education. Whether read sequentially or consulted for situational advice, this book serves as an essential resource for advancing sustainable and impactful EL practices.

Research and policy have become good at naming the cognitive risks that generative AI (GenAI)poses to learning. Namely, detrimental cognitive offloading, metacognitive laziness, and theillusion of competence. What this literature does much less well is tell teachers what to do withthat knowledge in their everyday practice. This article argues that alongside AI literacy, regulationand safeguards protecting educational stakeholders, one of the central challenges facingeducation in the age of GenAI is related to didactic design. When a finished text or answer nolonger reveals what a student has understood, struggled with, revised, verified or learned,teaching and assessment have to be redesigned so that the process of thinking is madevisible and necessary again. We argue that this redesign should be approached through theclassic didactic questions: why, what, how, when and for whom. We connect these questions withQuestion Zero (Q0), an approach to responsible technology adoption, through the dimensions ofwhy, who, what, how and where. Both didactic and Q0 perspectives and underlying questionsmove the starting point from the technology to educational purpose, stakeholders and theconditions under which the use of selected technology is well-designed and justified. We arguethat current AI strategies, guidelines and institutional frameworks in education leave aconspicuous gap precisely here. We translate these didactic and Q0 perspective questions into aconcrete, phased design for teaching and assessment, whose guiding principle is not to forbidcognitive offloading but to regulate it didactically. We also argue that making the learning processvisible is not in itself a neutral act, and that its design must attend to who carries its costs. Thischallenge recurs across education systems globally and cannot be solved by top-down regulationalone. Instead, translating high-profile principles of ethical AI in education into pedagogicallysound use of GenAI requires the contextual and professional judgement of educators.

This submission to the United Nations on Global Dialogue on AI Governance argues for a shift from technology-led AI governance towards purpose-led governance that prioritises societal value, human rights and long-term sustainability. It emphasises that decisions about AI should begin with assessing whether AI is appropriate in a given context before considering how it should be implemented. The submission calls for governance approaches that are human-centred, evidence-based and grounded in democratic principles.

Key priorities include trustworthy AI, capacity building, human rights protection, transparency, accountability and interoperability across governance frameworks. The authors highlight concerns about growing inequalities in access to computing resources, data and expertise. They also draw attention to governance gaps in transboundary domains and the need to address the social, cultural and institutional impacts of AI adoption.

The submission advocates for stronger international cooperation through inclusive and participatory governance processes. It stresses the importance of meaningful involvement from underrepresented communities, Indigenous peoples and actors from the Global Majority. Practical tools, policy literacy initiatives and structured self-assessment tools are presented as ways to support responsible AI adoption. The submission concludes that effective AI governance requires coordinated action, capacity building and continuous reflection on the purposes, risks and consequences of AI systems.

Dominant approaches to explainability in AI emphasise post hoc technical transparency, overlooking the socio-technical contexts in which systems are developed, deployed, and experienced. This paper argues that beginning AI adoption processes with Question Zero (Q0), “Should we adopt an AI system in the first place?”, reframes explainability as an essential requirement across the entire AI lifecycle rather than a narrow compliance task. Q0 challenges entrenched techno-solutionist assumptions that position AI as the default or best option, encouraging early integration of considerations of explainability in system design. By foregrounding this, Q0 shifts explainability towards purpose aligned, stakeholder aware forms that move beyond generic model centred outputs. Further, the paper reflects on how explainability can also strengthen Q0 by providing tools to assess the proportionality of AI adoption, clarify problem framing, and make visible the alternatives excluded during design. Taken together, these dual perspectives, Q0 for explainability and explainability for Q0, offer multidimensional opportunities for enhancing explainability. The paper illustrates this argument through reflection on the need, and value of, applying QO in the context of the European Commission’s Apply AI Strategy.

The Digital Omnibus Proposal aims to streamline the European Union’s digital regulatory framework but raises important concerns. This paper highlights risks related to reduced traceability of AI training data, weakened links between data governance and high-risk classification, and potential inconsistencies arising from simplified data access and reporting mechanisms. It argues that these changes may undermine effective risk assessment, shift complexity to downstream actors, and create legal uncertainty. To address these issues, the paper proposes targeted recommendations, including enhanced transparency and notification requirements for AI training data, safeguards to ensure that data availability does not affect risk classification, ex ante assessments for high-risk data reuse, and stronger governance and accountability measures for the centralised incident reporting system. These measures aim to preserve regulatory coherence, risk sensitivity, and the EU’s broader objectives of trustworthy and sovereign AI governance.

This perspective argues that the legitimacy of the UN Independent International Scientific Panel on Artificial Intelligence (IISPAI) depends as much on the demonstrable independence of its governance as on the quality of its scientific expertise. While the Preliminary Report offers an important contribution to global AI governance, it provides limited transparency regarding the management of funding, conflicts of interest, agenda-setting, and scientific disagreement. We contend that greater methodological and institutional transparency, alongside stronger structural safeguards for independence, is essential if the Panel is to serve as a trusted and authoritative source of scientific advice.

This paper examines the European Commission’s “AI First” strategy, arguing that it places acceleration and economic competitiveness above democratic values, societal benefit, and human-centric innovation. While substantial investment in AI is welcome when it promotes sustainable, equitable, and responsible innovation, the authors warn that policy is shifting from governance to unchecked deployment, risking fragmentation, dependency, and misaligned priorities. Rather than asking how AI can be applied, the paper urges policymakers to ask why, advocating a “People First” approach grounded in societal needs, digital sovereignty, and responsible innovation. The authors argue that Europe’s AI leadership should be shaped not by speed, but by principled direction, inclusivity, and a commitment to long-term public value.

Using signal detection theory, we meta-analyzed existing research testing the classification performance of supervised machine learning (ML) models applied in human resource (HR) contexts. Our meta-analysis contained 6605 effect sizes cross-nested by study (N1 = 249) and unique dataset (N2 = 152). We conducted separate cross-classified multilevel modeling analyses predicting six different classification performance indices. We tested hypotheses regarding the effects of algorithm type, sample size, number of predictors used, the number of outcome classes, and outcome class imbalance on model classification performance. Boosting and random forest algorithms performed best across classification performance indices. However, both come at relatively great computational expense, and solutions can be difficult to explain. Decision trees were the best-performing algorithms with relatively lower computational expense and easily interpretable solutions. We found limited evidence for the effect of sample size on classification performance. We found stronger support suggesting that using more predictors to train machine learning models results in better classification performance, but only when those predictors have substantive value. Finally, we found strong evidence that outcome class imbalance influences classification performance indices differently. More imbalanced outcome classes are associated with higher accuracy scores, which can be misleading, and lower precision and F1 scores, which may be better estimates of true classification performance. Our findings provide valuable insights into developing ML tools better suited to support HR functions.