Daniel Narh Treku

Artificial intelligence is a rapidly evolving technology whose capabilities are expanding quickly, leaving instructors across higher education to determine how to integrate it into teaching with little shared guidance. This paper reports on a Faculty Learning Community comprising seven faculty members from computer science, management information systems, systems engineering, economics, development studies, political science, and geography at a technological university in the northeastern United States. Through collaborative autoethnography, the group developed a convergence–divergence framework for AI-integrated pedagogy, visualized as a daisy: a shared core of AI literacy, ethical and risk awareness, and governance frameworks, surrounded by discipline-specific petals reflecting each field’s distinct conceptualization and application of AI. Seven disciplinary vignettes illustrate the framework in practice, revealing a common pattern across disciplines: AI tools offer possibilities and efficiencies while simultaneously obscuring risks and reproducing biases that require deliberate human attention. Drawing on ecological systems theory, the framework offers faculty a multi-level structure for designing AI-integrated courses that honor both shared foundations and disciplinary authenticity.

Artificial intelligence is a rapidly evolving technology whose capabilities are expanding quickly, leaving instructors across higher education to determine how to integrate it into teaching with little shared guidance. This paper reports on a Faculty Learning Community comprising seven faculty members from computer science, management information systems, systems engineering, economics, development studies, political science, and geography at a technological university in the northeastern United States. Through collaborative autoethnography, the group developed a convergence-divergence framework for AI-integrated pedagogy, visualized as a daisy: a shared core of AI literacy, ethical and risk awareness, and governance frameworks, surrounded by discipline-specific petals reflecting each field's distinct conceptualization and application of AI. Seven disciplinary vignettes illustrate the framework in practice, revealing a common pattern across disciplines: AI tools offer possibilities and efficiencies while simultaneously obscuring risks and reproducing biases that require deliberate human attention. Drawing on ecological systems theory, the framework offers faculty a multi-level structure for designing AI-integrated courses that honor both shared foundations and disciplinary authenticity.

Pervasively, organizations are using artificial intelligence (AI) to augment and automate business processes. Meanwhile, ethical concerns have been raised regarding the ability of algorithms to replicate existing human biases. To this end, a plethora of technical solutions have been proffered to address algorithmic discrimination. However, according to some studies, algorithms that prioritize fairness can be less accurate in their prediction outcomes, eliciting debates about the nature of the trade-off between accuracy and fairness in deploying fair algorithms. In this study, we explicate the contexts surrounding the socalled accuracy-fairness trade-off and make the empirical case for why, when, and how the trade-offs manifest in AI systems. Using Python-generated synthetic data for the flexibility of manipulating data features, we propose a classification framework to aid the understanding of the algorithmic accuracy-fairness trade-off. Besides the theoretical contribution, our study has practical implications for designing and implementing efficient and equitable AI systems.

Technology-induced changes to workflows may not necessarily lead to user satisfaction with solutions expected at the end of each workflow. This eventually affects project success. The study investigates user expectation and confirmation regarding cognitive agreement with the changes from old paper workflows to new electronic workflows. In particular, a research model is developed with two mediated routes between user expectation and solution satisfaction: one route is via perceived workflow agreements and the other via perceived performances. Based on longitudinal survey data of 118 participants in an institution that recently underwent a major transition in workflows, it was found that old workflow agreement influenced solution expectation more than old perceived performance, but new perceived performance impacted solution satisfaction more than new workflow agreement.