2026· AI-Enhanced Learning· Vol 2, pp. 307-333· 0 citations
TL;DR
A faculty-led process for identifying, prioritizing, refining, and recommending AI-enhanced learning use cases for leadership consideration offers a transferable model for managing innovation overload, evaluating innovation potential, and advancing responsible AI-enhanced learning.
Abstract
Background: Generative artificial intelligence has accelerated the diversity of proposed educational innovations, creating a practical challenge: how to evaluate, compare, and prioritize AI-enhanced learning ideas transparently. Existing guidance often emphasizes instructor-level decisions, while fewer models address institutional prioritization.
Purpose: This case study describes a faculty-led process for identifying, prioritizing, refining, and recommending AI-enhanced learning use cases for leadership consideration.
Approach: A teaching and learning AI task force representing seven professional schools developed a five-stage pipeline: structured ideation, transparent scoring and prioritization, deliberation and consensus building, use case refinement, and leadership-facing submission. This workflow supported proposal comparability, disciplined prioritization, and equity review before institutional decision-making.
Outcomes: The task force evaluated 24 proposals and 234 scored entries. Bounded AI-assisted analysis supported score synthesis, comment-theme identification, and thematic grouping, informing rather than replacing faculty decision-making. Following deliberation, related high-priority proposals were synthesized into three categories for refinement: course-grounded virtual teaching assistants, simulation practice engines, and faculty augmentation systems.
Implications: The pipeline offers a transferable model for managing innovation overload, evaluating innovation potential, and advancing responsible AI-enhanced learning.
Artificial intelligence (AI) is changing how educational institutions interact with donors, alumni and stakeholders. While early applications focused on automating routine tasks, the emergence of generative AI (GenAI) and large language models (LLMs) has introduced a new paradigm: AI as a strategic thought partner. This paper explores how GenAI can enhance leadership, creativity and decision making within university advancement, drawing on personal experience during a large-scale university merger and the applied use of multiple LLMs — including Microsoft Copilot, Claude and ChatGPT — as strategic tools. It presents principles of effective prompt design and use of ‘personas’ that enable advancement professionals to collaborate productively with AI and proposes a values-led advancement AI governance framework to support ethical and responsible use. The discussion highlights both opportunities and risks — emphasising the importance of human review, data privacy and the need for advancement-specific guidelines. The paper concludes that GenAI, when implemented with clear guardrails and institutional integrity, has the potential to elevate advancement from an operational function to a strategic, mission-aligned partner in shaping the future of higher education. This paper is also included in The Business & Management Collection, which can be accessed at https://hstalks.com/business/.
Liz Hawkins· Journal of Education Advance...· 0 citations
This case study explores a leadership-driven institutional response to the challenges posed by generative artificial intelligence (AI) to academic integrity within an English for Academic Purposes (EAP) foundation programme in Kazakhstan. The context involves academic essay writing assessments suddenly becoming vulnerable to AI-assisted plagiarism via large language models like ChatGPT or DeepSeek. The evolving initiative transitioned through three distinct phases in development: an initial zero-tolerance policy toward AI use in written assignments, a subsequent developmental phrase emphasizing awareness raising and support, and a third phase involving the proactive integration of AI into the programme via the redesign of assessment and materials. Drawing on literature related to academic integrity and AI to support the author’s self-reflection, this paper draws on lessons learnt from the experiences of a case study institution outside of the Western-centric context that is often targeted for research. The investigation critically evaluates the tensions between deterrence, awareness-raising, and curriculum adaptation. Findings suggest a need for a reflective and continuing state of adaptation to the challenges of AI on academic integrity. The study contributes to the emerging EAP discourse on AI by illustrating how programme leadership can shift from reactive policies to more sustainable and constructive pedagogical integration. The conclusions also recognize the ongoing need for change and the resulting faculty fatigue drawbacks that this constant state of fluidity can bring.
Alejandro Acuyo Cespedes· BALEAP Journal of Research a...· 0 citations
Artificial intelligence (AI) is transforming the global workforce, automating routine tasks, enhancing decision-making, and redefining job roles. This shift creates opportunities and uncertainties, demanding a reimagined approach to education. Traditional models, rooted in static curricula, fail to prepare learners for a dynamic, AI-driven landscape. This abstract proposes strategic learning pathways to foster adaptability, critical thinking, and human-AI collaboration, drawing from education, cognitive science, and workforce development. AI technologies like machine learning and robotics are reshaping industries, automating tasks like data entry while creating roles requiring AI oversight and creative problem-solving. The World Economic Forum (2023) estimates over 50% of jobs will need reskilling by 2030 due to AI disruptions. Traditional education, designed for predictable careers, struggles to keep pace, risking a skills gap that threatens economic stability. Current systems emphasize rote learning and specialized skills, ill-suited for an AI-augmented world. STEAM-focused curricula often neglect interdisciplinary thinking, emotional intelligence, and ethical reasoning, while unequal access to education exacerbates inequities. A shift toward agile, inclusive education is essential to prepare learners for uncertainty. We propose three pillars: Adaptive Competence: Prioritize cognitive flexibility and problem-solving through scenario-based, interdisciplinary learning; Human-AI Synergy: Train learners to leverage AI tools, understand biases, and apply human judgment; Lifelong Learning Ecosystems: Integrate micro-credentials and accessible platforms for continuous upskilling; Systemic changes—competency-based curricula, educator training, and public-private partnerships—can align education with workforce needs. While challenges like institutional resistance persist, these pathways empower resilient, inclusive learning, preparing individuals to thrive in an AI-driven future.
A. E. Adesina· Aminu Kano Academic Scholars...· 0 citations
AI's ability to change educational leadership and supervision has garnered attention due to its rapid expansion. Early research has examined AI applications in educational administration, leadership decision-making, and learning analytics, but research on its effects on educational supervision is fragmented. Authors will map the current state of AI in educational supervision and leadership, identify new trends, potential, and issues, and provide an integrated conceptual framework for future study and practice. PRISMA-ScR scoping review was used in this investigation. A comprehensive search of major academic databases yielded 29 publications published between 2021 and 2026 that met the inclusion criteria for final analysis. The results were analysed using thematic coding and six domains: supervisory transformation, human–AI collaboration, data-driven governance, sociotechnical preparation, ethical governance, and future-oriented innovation. The results show that AI is making instructional supervision data-driven, predictive, and collaborative. The literature emphasises the growing importance of human–AI collaboration, learning analytics, evidence-based decision-making, and ethical governance in educational growth. However, digital preparation, algorithmic transparency, data privacy, and organisational competency issues hinder uptake. The AI-Supervision Framework, an integrative model of AI-enabled educational supervision as a human-centered ecosystem involving technology, data, leadership, organisational preparation, and ethical governance, is presented in this review. This review contributes to the field by consolidating fragmented scholarship on AI, educational leadership, learning analytics, and instructional supervision into a coherent evidence base. It advances theoretical understanding by conceptualising AI-enabled supervision as a human-centred sociotechnical ecosystem and provides an analytic foundation for future empirical research.
Rachmat Satria· International Journal of Mul...· 0 citations
A three-level meta-analysis of 22 controlled studies found significant effects across four learner-internal outcome domains, including learning achievement and skill performance, higher-order thinking, affective and motivational outcomes, and collaboration and communication outcomes.
The rapid integration of artificial intelligence (AI) into educational settings presents a profound challenge for teacher education. This study examines how pioneering teacher educators in Israeli colleges of education perceive their role in training student teachers for AI-integrated teaching. Using a qualitative, interpretive phenomenological approach, semi-structured interviews were conducted with 13 participants—seven pedagogical advisors and six lecturers—from seven teacher training institutions representing diverse educational streams. Data were analyzed using a hybrid approach combining human thematic analysis and AI-assisted dialogic analysis. Findings revealed that participants perceived themselves as agents of change responsible for modeling critical AI use, while raising epistemic concerns regarding cognitive atrophy and the erosion of expertise. A crisis of trust triggered by students’ uncritical submission of AI-generated outputs prompted a shift toward process-based and in-class assessment. Pre-service teachers used AI for differentiated lesson planning, visual aids, and simulations, with experiences ranging from heightened self-efficacy to frustration. Persistent challenges included rapid technological change, the absence of institutional policy, ethical and privacy concerns, cultural factors, and economic barriers. Drawing on post-digital theory and AI literacy frameworks, this study argues that effective preparation for the AI era requires a fundamental reimagining of teacher education—one that redefines literacy, pedagogy, and human agency.
Olzan Goldstein, Nareman Marae-Haj, Wafa Zidan· AI in Education· 0 citations
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