This case study explores Our Lady of the Lake University’s (OLLU) strategic initiative to enhance STEM education for Hispanic students, a project supported by a Title V DHSI grant. Facing challenges in STEM retention and success, OLLU implemented a multifaceted approach, including needs assessments to pinpoint student-specific obstacles. Key programmatic strategies, such as Universal Design for Learning (UDL), Experiential Learning (EXL), and Writing to Learn techniques, were employed to redesign core STEM courses. The article will detail the implementation process, highlighting the establishment of the EXL STEM Studio and the subsequent redesign of the courses. Outcome analysis will focus on demonstrable impacts: student retention, graduation rates, and the increase in STEM degrees awarded to Hispanic students. This case study offers valuable insights for HSIs seeking to improve STEM outcomes, emphasizing the effectiveness of tailored academic support and innovative pedagogical approaches.
Diverse and inclusive teams are increasingly recognized as critical drivers of innovation, productivity, and organizational resilience in corporate environments. However, forming balanced and effective teams remains a persistent challenge for human resource managers and project leaders. Traditional approaches—such as self-selection, managerial assignment, or random allocation—often fail to adequately account for the complex interplay of employees’ skills, social ties, and organizational goals. To address this gap, we present a novel, AI-driven system that automates the formation of heterogeneous corporate teams by integrating genetic algorithms with social network analysis. The system enables managers to define project requirements, specify required skills, collect employees’ self-assessments, and visualize workplace social networks through an intuitive interface. The hybrid algorithm optimizes three critical objectives simultaneously: maximizing skill diversity, enhancing workplace cohesion through social ties, and maintaining balanced team sizes. Using simulated organizational networks of varying scales, we demonstrate the system’s reliability, robustness, and adaptability in producing inclusive and balanced teams. This approach has strong potential to reduce biases in team formation and empower organizations to align workforce composition with strategic objectives.
The increasing complexity of healthcare environments requires innovative educational approaches to better prepare students and professionals for the realities of clinical practice. Traditional lectures and limited role-play simulations often fall short in providing repeated, standardized, and unbiased opportunities to practice essential communication skills. At e-REAL Labs, we have developed Conversation Mastery™, a proprietary platform powered by AI-driven avatars—embodied conversational agents (ECAs)—specifically designed to enhance virtual coaching in medical education. These avatars recreate difficult and emotionally charged clinical conversations, offering real-time adaptive feedback and fostering critical skills such as conflict resolution, empathy, and inclusive communication. Unlike generic AI coaching tools, the system integrates generative AI, natural language processing, and behavioral analytics into a controlled and curriculum-aligned framework. Learners benefit from personalized, structured feedback that directly supports the acquisition of clinical competencies and professional readiness. In collaboration with the Harvard Center for Medical Simulation, we conducted a controlled study comparing lecture-based instruction, traditional role-play, and AI-driven avatar training among nursing students. Results demonstrate that students training with avatars achieved significantly higher success in conflict de-escalation, improved confidence, and stronger adherence to best practices. By embedding Conversation Mastery into simulation-based education, institutions can expand practice opportunities, ensure biasaware evaluation, and support compliance with GDPR, HIPAA, and emerging AI governance frameworks. This paper extends previous work presented during The Learning Ideas Conference 2025 (held at Columbia University in New York City and online) by providing a deeper exploration of the pedagogical, ethical, and regulatory implications of integrating AI-driven avatars into healthcare training. We argue that these technologies represent a new paradigm for clinical readiness, complementing faculty expertise while scaling access to highquality, adaptive, and equitable learning experiences.
Learning and development (L&D) units play a central role in advancing organizational goals. The emergence of generative AI (GenAI) presents both an opportunity and a disruption to traditional models of workforce training, requiring L&D leaders to rethink strategies for learning design, change management, and continuous upskilling. In this paper, we identify transferable insights from the higher education context that can inform corporate strategies. We draw from survey data of faculty and students at the NYU School of Professional Studies, combined with an analysis of industry-focused reports and case studies. More specifically, L&D teams are uniquely positioned to provide guidance and support for workforce development that empowers employees to use GenAI tools in ways they trust and understand. We encourage L&D teams to engage new and existing employees with opportunities to build trust, rethink bottom-up approaches, and center AI-Human partnerships. Creating opportunities for employees to use AI technologies in ways that build on their knowledge and experiences, Human-centered AI (HCAI) L&D strategies ensure that GenAI adoption is designed as a force multiplier rather than a replacement.
The rapid integration of Generative AI (Gen AI) into education has raised fundamental questions about its influence on learning, prompting debate on whether it represents a new learning paradigm. This inquiry exploration contributes to the discussion on whether the unique dynamics of learning with Gen AI implies a new learning paradigm. By establishing the criteria for a paradigm shift, we analyzed the anomalies that Gen AI creates for established traditional paradigms (i.e., Behaviorism, Cognitivism, and Constructivism) demonstrating their insufficiency to explain the dynamics of the human learning with Gen AI. Hence, we propose and formally define a new paradigm in which learning is understood as a distributed process enacted through a human engagement with Gen AI-artifact system. In this model, the learner acts as an orchestrator, guiding iterative sequence of prompting, generation, critique, and refinement. The new paradigm will provide educators and researchers with a coherent conceptual language to design and study learning in the age of Gen AI. The study concludes by outlining verifiable suggestions to guide future empirical validation of this new paradigm.
Education and professional training are increasingly challenged by a persistent gap between knowledge acquisition and the ability to perform effectively in real-world contexts. While digital learning, extended reality (XR), and artificial intelligence (AI) have expanded access to education, many implementations still replicate transmissive pedagogical models, offering limited support for decision-making, adaptability, and professional judgment. This paper argues that the central challenge is not technological adoption per se, but the redefinition of readiness as a dynamic, systemic, and measurable construct. We propose a readiness-oriented framework in which learning environments are designed to support cognitive, emotional, and social preparedness through deliberate practice, reflective feedback, and psychologically safe experimentation. Within this framework, AI-driven conversational avatars are conceptualized not merely as instructional tools but as mediating agents that scaffold learning, reduce the emotional cost of errors, and enable repeated exposure to complex, high-stakes scenarios. By combining immersive environments, adaptive dialogue, and analytics-driven feedback, avatar-mediated simulations support the transfer of learning across contexts and cultures. Drawing on empirical evidence from multi-year studies, cross-sector case analyses, and survey-based research, the paper examines how readiness-oriented, multimodal learning ecosystems enhance engagement, accelerate skill acquisition, and improve decision-making under pressure. The discussion highlights implications for higher education, healthcare, corporate training, and leadership development, emphasizing inclusivity, bias mitigation, and ethical alignment. Ultimately, the paper positions readiness—not content mastery—as the primary outcome of contemporary education, and AI-driven avatars as a key enabler of scalable, equitable, and practice-centered learning.
Serendipity is at the heart of innovative ideas and strategic opportunities and is crucial for business success. Cultivating serendipity has become a growing priority for many organizations, typically by redesigning work environments and introducing aligned incentive schemes. These strategies, however, often fail to deliver the expected results. Given this, we propose leveraging GenAI-enabled dialogic training as an alternative. In this new paradigm, GenAI supports individuals in engaging in dialogue that creates psychologically safe space for them to co-evolve with diverse and unknown ideas. By engaging in the dialogic space, individuals develop the skillsets to question assumptions, challenge preconceptions, and critically assess new ideas, all of these are also core for developing a serendipitous mindset that enables individuals and corporates be ready to recognize, embrace, and harness serendipity effectively and sustainably.
Kaizen is a Japanese word which translates to change or improvement, and it is the cornerstone of several process improvement systems. In the learning and development (L&D) space, we are always seeking ways to improve and become more efficient and effective. This case study updates a previous one from two years ago. Through the kaizen of our onboarding and training program, we have seen an increase in employee longevity after completing our five-day new hire onboarding program. This improved program integrated a flipped classroom design and inclusion of multiple learning management system (LMS)-based courses, among other changes. We also gave managers choices for their new employees’ onboarding program. In addition to double the number of employees being hired and onboarded, we also saw an increase of 6 percent in employees staying beyond the 180-day mark. We offer some suggestions to help streamline the onboarding process even further.
The notion that computers equipped with software and artificial intelligence (AI) applications can effectively replace human intelligence is under the existing paradigm of operations. This is the leading facilitator of task automation on board a ship to fulfill various jobs and is often supported by numerous scientists/academics who contribute to gaining today’s momentum in public opinion. The discourse of Industry 4.0 in the maritime domain, however, tends to focus heavily on technological contexts and overlooks socio-economic contexts. This gap is evident in the strong emphasis on technical and digital skills for future maritime professionals. This paper, therefore, argues that the socio-economic context of Industry 4.0 can be understood as fostering soft skills for future seafarers. In particular, our focus is on emotional intelligence concerning “future skills” on demand in the maritime industry. This paper reviews relevant theories and conceptualizes how integrating emotional intelligence into maritime education and training can support the transition toward maritime digitalization. The main objective of this paper is to present a framework that effectively incorporates emotional intelligence into a teamwork environment suitable for the ship’s bridge and to outline a set of skills training based on Transformative Learning.
In this paper, we study permutations on n elements that are even on every subset of size t. We describe all groups of these permutations. Unexpectedly, these groups (except for some special cases) are either trivial, cyclic or dihedral. In this context, we define and study monoids that generalize both monoids of order-preserving mappings and monoids of orientation-preserving mappings.
We provide an infinite family of sofic one-relator groups that are not residually solvable nor residually finite. The proof is essentially different from the one in [1], as it does not require just Magnus' decompositions.
Extending Sparks's theorem, we determine the cardinality of the lattice of (C1,C2)-clonoids of Boolean functions for certain pairs (C-1,C-2) of clones of essentially unary, linear, or 0- or 1-separating functions or semilattice operations. When such a (C-1,C-2)-clonoid lattice is uncountable, the proof is in most cases based on exhibiting a countably infinite family of functions with the property that distinct subsets thereof always generate distinct (C-1,C-2)-clonoids. In the cases when the lattice is finite, we enumerate the corresponding (C-1,C-2)-clonoids. We also provide a summary of the cardinalities of (C-1,C-2)-clonoid lattices of Boolean functions.
Given a computably locally compact Polish space M, we show that its 1-point compactification M & lowast; is computably compact. Then, for a computably locally compact group G, we show that the Chabauty space & Sscr;(G) of closed subgroups of G has a canonical effectively-closed (i.e., Pi 10) presentation as a subspace of the hyperspace K(G(& lowast;)) of closed sets of G(& lowast;). We construct a computable discrete abelian group H such that S(H) is not computably closed in K(H-& lowast;); in fact, the only computable points of S(H) are the trivial group and H itself, while S(H) is uncountable. In the case that a computably locally compact group G is also totally disconnected, we provide a further algorithmic characterization of S(G) in terms of the countable meet groupoid of G introduced recently by the authors (arXiv:2204.09878). We apply our results and techniques to show that the index set of the computable locally compact abelian groups that contain a closed subgroup isomorphic to (& Ropf;, +) is arithmetical.
In this second paper, we solve the twisted conjugacy problem for even dihedral Artin groups, that is, groups with presentation G(m) = < a, b |(m)(a, b) = (m)(b, a)>, where m >= 2 is even, and m(a, b) is the word abab & mldr; of length m. We then prove orbit decidability for the full automorphism group Aut(G(m)), which then implies that the conjugacy problem is solvable in certain extensions of even dihedral Artin groups.
This paper addresses the innovative strategies adopted by the DigIN project in order to increase digital literacy among adults aged 55 years and above using social media platforms, particularly YouTube and Spotify. The project will create an active learning community by sharing relevant educational content tailored to meet the needs of older learners. Analytics gathered data from all platforms, showing key performance indicators, including views and engagement rates and insight into demographics. Preliminary findings indicate high engagement; for example, on the YouTube channel, there were 2120 views and a total of 42.9 hours of watching time, with 42% of viewers falling into the age group 55–64. What is more, those podcasts published on Spotify were highly rated, especially among Polish speakers, thus proving great interest in digital competencies locally. Results prove that social media can be quite a useful tool for corporate learning and human resources development, with the aim of singling out the need for tailored content, taking into consideration peculiar challenges that, in general, arise while teaching older adults. The implications, therefore, for future corporate training initiatives lie in the use of social media to create learning environments that empower underserved populations and, by extension, increase their independence and overall workforce competency.
As organizations strive to navigate the complexities of a globalized workforce, professional development plays a crucial role in future-proofing operations. This study examines the challenges and opportunities associated with language proficiency, focusing on Native English Speakers (NES) and English as a Second Language (ESL) professionals. By identifying disparities in the design, delivery, and assimilation of training programs, the research explores how language proficiency can serve as both a barrier and facilitator to professional growth. The study highlights the need for inclusive development strategies that address linguistic and cultural challenges while fostering adaptability within organizations. Key stakeholders, including leadership, human resources, educational institutions, and technology providers, are essential in creating a collaborative framework that ensures equal growth opportunities. The ultimate goal is to uncover best practices that promote innovation, inclusivity, and long-term organizational success by integrating diverse talent pools and enhancing training outcomes across a multilingual workforce. Through this strategic approach, organizations can cultivate leadership and agility to thrive in an evolving global market.
This case study explores an innovative e-learning ecosystem for legal training in local government that integrates structured video lectures, interactive quizzes, and personalized microlearning powered by Micromate, an AI conversational learning assistant. Micromate complements complex legal topics—such as procurement law and housing benefit law—with accessible five-question chat-based microlearning sessions. The system uses generative AI to auto-create quiz sessions, analyzes individual forgetting curves, and adapts to each learner’s behavior. Five-question sessions fit seamlessly into daily routines, while gamification elements such as badges sustain motivation. Content is dynamically unlocked based on course progress, encouraging continuous engagement. Deployed across three pilot courses, Micromate achieved 70% voluntary participation among eligible staff, over 12,000 quiz questions answered during the six-month pilot phase across three e-learning-courses, and a 300% increase in weekly questions (800→2,400 from week 1 to 28). This comprehensive approach redefines professional development in public administration by creating efficient, engaging, and personalized learning experiences that enhance knowledge acquisition and retention
This study explores the evolving landscape of microlearning by synthesizing peer-reviewed literature and real-world insights from instructors and learners affiliated with the online learning platform GoSkills. Using a qualitative, exploratory design, it examines how microlearning is defined, delivered, and experienced across corporate and educational settings. A review of 13 academic sources highlights key dimensions such as brevity, adaptability, targeted instructional design, and emerging digital formats. Semi-structured interviews with three instructors and three learners complement this review, offering a grounded perspective on how these concepts take shape in practice. While microlearning offers clear advantages in flexibility, motivation, and knowledge retention, its conciseness may limit deeper learning unless supported by thoughtful scaffolding or contextual aids. By drawing on recent literature and practitioner experiences, this study contributes to a better understanding of microlearning’s current strengths and challenges and outlines future research directions, including its integration into broader learning ecosystems and the evolving role of AI-powered learning technologies.
This study explores integrating artificial intelligence (AI) tools in academic workflows among management faculty at universities in Poland’s Silesia region. Using a mixed-methods approach—combining quantitative surveys (n = 352) and qualitative interviews (n = 15)—the research examines how demographic factors such as age, academic rank, and prior technical experience influence the adoption, perceived benefits, and ethical concerns surrounding AI in scholarly work. Findings reveal substantial demographic disparities in AI usage. While 68% of participants report using AI tools for academic tasks, adoption is higher among younger (74% for ages 18–22) and male faculty (75.7%) compared to older (58% for ages 23–28) and female faculty (60.7%). Higher academic rank and technical proficiency also correlate with more advanced use of AI in research. Master’s-level faculty perceive more significant benefits from AI (β = 0.31, p < 0.01) than Bachelor’s-level peers. Concerns persist despite these benefits, such as improved efficiency in literature synthesis, data analysis, and manuscript preparation. Many respondents express ethical apprehensions regarding intellectual integrity, data privacy, and algorithmic bias. These are especially of concern to female academics and those utilizing qualitative methods. Institutional support is the most significant motivator of using AI responsibly. Participants cite the lack of ethical guidance, training, and policy within discipline fields as the hindrances. The study calls for explicit citation standards, targeted AI literacy programs, and comprehensive institutional frameworks to ensure the responsible integration of AI in academic settings. Three main hypotheses guide this research: younger students are more likely to use AI tools, master’s-level students perceive higher utility in AI, and better institutional support predicts more responsible AI use. These hypotheses are tested quantitatively and further illuminated through qualitative data, contributing to academic theory and practical policy-making for post-industrial regions like Silesia.
The Creative–Analytical–Managed (CAM) Learning Model, a unique pedagogical framework, is designed to meet the intricate learning needs of modern corporate environments. Traditional training methods fall short in digital transformation, globalization, and evolving labor markets. CAM, an evidence-based and holistic choice, integrates three interconnected components: Creative, Analytical, and Managed. The Creative component is dedicated to divergent thinking and innovative problem-solving; the Analytical component is committed to critical thinking and data analysis; and the Managed part is concerned with goal-setting, self-management, and project planning. These dimensions work harmoniously to support a dynamic learning process that aligns with adult learning theory and strategic human resource development. This research confirms the CAM model using a mixed-method study with 200 working adult learners from various corporate environments. Quantitative metrics such as confirmatory factor analysis and regression models show that the three dimensions significantly predict training outcomes, with the managed dimension having the greatest unique contribution. Erasmus+ students’ quantitative feedback also validates the viability of the model, attributing increased participation, creative confidence, critical thinking, and project delivery. The CAM model’s flexibility can be realized in various training settings such as digital upskilling, entrepreneurship, leadership, and off-site onboarding. It complements AI-enhanced learning by emphasizing uniquely human capabilities—creativity, judgment, and strategic autonomy—that resist automation. Practically, CAM offers a scalable solution for Human Resources professionals and learning designers seeking to build future-ready, self-directed learners. Theoretically, it bridges critical pedagogy, adult learning, and projectbased education. In spite of the difficulties of implementation, the empirical robustness and diversity of CAM support its continuance as a means of meeting the demands of learning in a progressively sophisticated work setting, solidifying trust in its effectiveness.
