Understanding the place and value of GenAI feedback: a recognition-based framework

We consider a framework for sequential bilevel linear programming in which a leader and a follower interact over multiple time periods. In each period, the follower observes the actions taken by the leader and reacts optimally, according to the follower’s own objective function, which is initially unknown to the leader. By observing various forms of information feedback from the follower’s actions, the leader is able to refine the leader’s knowledge about the follower’s objective function and, hence, adjust the leader’s actions at subsequent time periods, which ought to help in maximizing the leader’s cumulative benefit. We show that greedy and robust policies adapted from previous work in the max-min (symmetric) setting might fail to recover the optimal full-information solution to the problem (i.e., a solution implemented by an oracle with complete prior knowledge of the follower’s objective function) in the asymmetric case. In contrast, we present a family of greedy and best-case policies that are able to recover the full-information optimal solution and also provide real-time certificates of optimality. In addition, we show that the proposed policies can be computed by solving a series of linear mixed-integer programs. We test policy performance through exhaustive numerical experiments in the context of asymmetric shortest path interdiction, considering various forms of feedback and several benchmark policies.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

This paper presents a systematic review of the role of prompt engineering during interactions with Generative Artificial Intelligence (GenAI) in Higher Education (HE) to discover potential methods of improving educational outcomes. Drawing on a comprehensive search of academic databases and relevant literature, key trends, including multiple framework designs, are presented and explored to review the role, relevance, and applicability of prompt engineering to purposefully improve GenAI-generated responses in higher education contexts. Multiple experiments using a variety of prompt engineering frameworks are compared, contrasted and discussed. Analysis reveals that well-designed prompts have the potential to transform interactions with GenAI in higher education teaching and learning. Further findings show it is important to develop and teach pragmatic skills in AI interaction, including meaningful prompt engineering, which is best managed through a well-designed framework for creating and evaluating GenAI applications that are aligned with pre-determined contextual educational goals. The paper outlines some of the key concepts and frameworks that educators should be aware of when incorporating GenAI and prompt engineering into their teaching practices, and when teaching students the necessary skills for successful GenAI interaction.

Evaluating generative AI responses to real-world drug-related questions

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

The integration of Fast Healthcare Interoperability Resources (FHIR) with generative artificial intelligence (GenAI) systems is accelerating across the healthcare landscape, particularly within pharmacy ecosystems responsible for medication dispensing, formulary management, prior authorization, and real-time benefit determination. While FHIR provides a standardized framework for interoperable data exchange, GenAI introduces new risks such as prompt injection, hallucinated clinical assertions, privacy violations, model inversion, and inadvertent leakage of protected health information (PHI). These risks become more pronounced when GenAI models are connected to high-sensitivity medication APIs used by payers, pharmacies, and clinical systems. This paper presents a comprehensive security architecture for FHIR-GenAI integration within pharmacy workflows. It proposes a multilayer strategy incorporating Zero Trust, privacy-preserving computation, API governance, model guardrails, and secure machine-learning pipelines. Code examples are provided to illustrate key enforcement mechanisms. The analysis demonstrates that secure implementation of GenAI in pharmacy systems must harmonize conventional enterprise security controls with new GenAI-specific risks while honoring regulatory requirements such as HIPAA, HITECH, and HITRUST CSF.

In this paper, an index for measuring the versatility of artificial general intelligence (AGI) systems is proposed. The index called Versatility Index (VI) is used to measure the versatility of an AGI system or for comparison purposes between different AGI systems. Then, an upgraded version of the original AGI Brain is proposed. In the new model, AGI Brain II, the explicit memory (EM) is replaced with a modified Mamdani fuzzy inference associative memory, called ProMem, which is able to estimate the consequences of a certain action by estimating the probability density function (PDF) of the observed data in a stochastic environment. The model was tested in a portfolio optimization scenario as a stochastic environment. Simulation results demonstrate the accuracy of the novel explicit memory as well as the increased versatility index of the upgraded model. KeywordsVersatility indexAGI BrainProMemStochastic environmentsExplicit memoryState/output estimatorFuzzy inference systemProbability density functionPortfolio optimization

If and when artificial intelligence systems become superhuman in more aspects of analytic reasoning, this will inevitably have a strong impact on the social organisation of science, including academic writing, reviewing, and publishing. We consider how norms of academic publishing should be adjusted as this happens. To do so, we propose four intuitively plausible desiderata that norms of academic publishing should fulfil in the age of increasingly advanced artificial intelligence (AI) and argue that there are no “quick fixes” to current norms that fulfil these desiderata. To indicate the scale of change needed to prepare academic publishing for the era of increasingly advanced AI, we tentatively sketch a more promising novel system of norms. Our proposal centres around the idea that AI systems should “sign off’’ on statements that outline the human and AI contributions to academic research. We discuss possible challenges for this proposal and highlight the type of technological and regulatory infrastructure that would be needed to enable it.

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

This study presents a comprehensive framework designed to enhance data security in artificial intelligence (AI) systems by integrating robust cybersecurity measures with information governance principles. Employing a convergent parallel mixed-methods design, the research combines a quantitative meta-analysis of 11,245 AI deployments, a qualitative synthesis of 89 governance studies, and technical validations across three open-source AI models (BERT, YOLOv7, and federated learning). Critical threats, including data poisoning (42% success rate), adversarial examples (23% higher vulnerability in vision models), and model inversion (31% more frequent in generative AI), were identified. The proposed framework demonstrated a Composite Security Score (CSS) of 0.87, outperforming existing models (CSS range: 0.78–0.82), and achieved 84–89% attack mitigation and 90–95% regulatory compliance under standards like GDPR and NIST RMF. The framework’s practical contribution lies in offering sector-specific, scalable solutions for strengthening AI system security, thereby enabling healthcare, finance, and public sector organizations to align innovation with ethical and legal obligations. Theoretically, the study advances knowledge by bridging cybersecurity engineering with governance structures, addressing a critical gap in AI risk management. Preliminary evaluations into quantum-ready encryption strategies were explored, though more extensive testing is suggested for future research. Key limitations include reliance on secondary datasets, potential model generalizability constraints, and emerging threats like quantum attacks that require ongoing adaptation. Recommendations advocate sector-specific adoption, quantum-ready encryption, and research into adaptive security and third-party risks to ensure scalable, ethical AI security.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

The advent of generative artificial intelligence (AI) represents a transformative leap in project management, offering significant capabilities in creativity and problem‐solving. This essay explores the impact of generative AI, highlighting its potential to support project planning and execution through dynamic resource management, improved stakeholder interaction, and predictive analytics. By understanding the differences between traditional AI and generative AI, project managers can harness these advanced tools to address a variety of challenges, from simple scheduling tasks to complex strategic planning. Generative AI enables project managers to interact with large language models (LLMs) through precise prompt engineering, ensuring tailored and actionable outputs. Practical examples illustrate how generative AI can provide innovative solutions to project delays and other common issues, showcasing its ability to transform traditional practices. Despite its advantages, the integration of generative AI in project management comes with significant risks, including job displacement, ethical concerns, data privacy, and the potential for overreliance on AI systems. Effective change management, robust ethics guidelines, and continuous learning are essential to mitigate these risks and maximize the benefits of AI. As project managers navigate this evolving landscape, they must remain proactive, balancing innovation with ethical considerations to lead their teams toward a future where AI‐driven project management is both effective and responsible.

Aim/Purpose In a post-pandemic learning environment, artificial intelligence (AI) may become the new standard. More and more college students are utilizing AI tools like ChatGPT and Bard to enhance their learning. This study compares the use of web-search and AI-based systems to assess how much students' knowledge has improved in an Internet Marketing course. Background While authorship and academic integrity have received a lot of attention in higher education research thus far, using AI effectively provides several benefits for both teaching and learning. In other words, by utilizing these capabilities, AI-based systems may boost students' motivation, interest, and level of knowledge. Methodology The course will teach the students how to use the application of AI (i.e. ChatGPT). To evaluate the efficacy of AI technology, this study will conduct empirical research to compare the knowledge improvement of students who would obtain knowledge about buying sustainable production products in Taiwan between using the web-search systems and the AI-based systems. The assessments of students' knowledge growth are evaluated by using a dependent samples t-test. Contribution This finding expands on prior research examining the possibility of using AI-powered learning aids to inspire students (Al Shamsi et al., 2022; Lund et al., 2023). The ability of ChatGPT to deliver cutting-edge content, personalized feedback, and interactive learning sessions may all contribute to generating curiosity, a sense of accomplishment, and ultimately a greater desire to learn more about subjects. Findings It is anticipated that the AI chatbots created for information delivery might have something to do with students' increased interest in, motivation for, and understanding of their studies. This study examines the potential benefits to students' understanding of sustainable development goals with an AI-specific method of information provision relevant to purchasing items produced sustainably. Recommendations for Practitioners In the near run, using AI systems has been proven to boost university students' sentiments. University students may feel more supported by AI when they use it more regularly. Long-term AI use may cause reliance, especially in the absence of human companionship. Recommendations for Researchers Not all students and instructors have a lot of experiences in teaching and learning with generative AI, despite the fact that it is a relatively new technology. After generative AI is completely incorporated into classrooms, the roles of AI in teaching and learning could reconsider. Impact on Society In order to prepare students for work in a world driven by generative AI, higher education in the future needs change. With an emphasis on in-class and hands-on activities for assessment, new learning outcomes—skills in learning and teaching with AI, AI literacy—and the importance of interdisciplinarity should be highlighted. Future Research Future studies should concentrate on the following topics: interdisciplinary education, creative pedagogies and their evaluation, new assessment and its acceptability.

A propositional structure is set forth that considers: (1) the role of various forms of feedback in eliciting differing motivational orientations toward performance in tasks that reflect ability; (2) the strength of arousal associated with these various motivational orientations as a function of repeated feedback and the age of the performer; and (3) the impact of differing strengths of arousal upon the performance of simple and complex tasks. On the basis of this propositional statement, a series of hypotheses are formulated and tested concerning predicted effects of various forms of feedback upon second, fourth and sixth grade Flemish boys performing simple and complex tasks in a classroom setting. In general, the results are consistent with predictions that the strength of competitive motives, induced by own and other performance feedback, as compared to individualistic motives induced by own feedback and a control condition of no feedback, increase both as a function of trials and the age of the performer, and that these increases in arousal lead to higher levels of performance in simple, but not complex tasks.

Generative artificial intelligence (GAI) has the potential to assist instructors by providing effective formative feedback to students. Focus and complexity are important characteristics of formative feedback. Few studies have examined the combined effects of these factors in online collaborative learning. This study explored the effects of focus and complexity of GAI-assisted formative feedback (GAI-FF) on students’ online collaborative engagement, shared metacognition, And learning performance. A total of 114 preservice teachers were recruited from a public university in northwest China and randomized into four experimental conditions: task level and simple, task level and elaborated, multilevel and simple, and multilevel and elaborated GAI-FF. Participants were provided with four types of GAI-FFs longitudinally, and their pre-and post-tests, discussion processes, and learning performance were measured and recorded. Analysis of Variance (ANOVA) and its nonparametric alternatives were performed, and we found that (1) multi-level GAI-FF significantly promoted objective behavioral engagement, but not cognitive, positive, or negative emotional engagement; (2) the focus, not the complexity of GAI-FF, significantly impacted group shared metacognition; (3) there was a significant interaction effect between focus and complexity of GAI-FF on group shared metacognition; and (4) preservice teachers in multi-level GAI-FF conditions significantly outperformed those in task-level conditions in terms of group artifacts and course performance. The theoretical and practical implications of optimizing GAI-FF in collaborative learning to support learning and performance are discussed.