The study explores the ethical concerns and the affordances of generative artificial intelligence (GenAI) in higher education from the perspectives of underserved students. Data were collected through a survey of 77 students in a public university with predominantly underserved students in the United States. Informed by the five principles of AI ethics and technology affordance theory, the study employed a mixed-methods approach. Qualitative analysis of the narrative data revealed six themes: utility affordances, value affordances, user behavior, AI use outcomes, the contingency of ethical AI use, and a pessimistic view of ethical AI use, suggesting that ethical considerations in GenAI use depended on user perspectives. Furthermore, quantitative analysis indicated that student perceptions of the ethical use of GenAI varied by their demographic and socioeconomic background. The study also revealed nuances of the digital divide in AI. The study contributes to the literature by proposing an integrated model of ethical GenAI use and offers practical implications for promoting effective and ethical use of GenAI in higher education.

Artificial intelligence (AI) is disrupting workforce and posing an unprecedented threat of job displacement. However, our understanding of AI's role in shaping individual career development is limited. This study provides insights into AI and career development within the context of first-generation college students (FGCSs), a marginalized group that is arguably among the most vulnerable to the career disruption of AI. Employing mixed methods, this exploratory study examines the effects of FGCS status and career anchor on individual concerns about AI’s career impact and the perceptions of FGCSs and non-FGCSs regarding their career development. Using survey data from 70 students at a minority-serving public university in the United States, the quantitative analysis shows that FGCS status is positively associated with individual concern about AI’s career impact, whereas prior ChatGPT experience is negatively associated with this concern. However, we did not find evidence that a student’s career anchor affects their concerns about AI’s career impact. Meanwhile, the qualitative analysis revealed four themes that highlight employed FGCSs’ reliance on college education to change to a professional career or prepare for entrepreneurship. Our follow-up study revealed four types of individual attitudes toward AI’s career impact and suggested that the attitudes are influenced by generational status and career stage. We compare FGCSs and their peers in terms of career stage, career development and attitude toward AI’s impact and propose intervention strategies to help FGCSs mitigate AI-related job replacement risks. The study contributes to research on the AI impact on career development of a marginalized population.

At a time when the value of higher education is increasingly being questioned, this editorial examines the need to re-position higher education for the future workforce. It argues that higher education itself is not the problem; rather, its prevailing structures may no longer be adequate for the realities of the AI era. The editorial explores the strategies institutions can pursue to survive and remain relevant amid the AI wave. In particular, it considers what structural changes may be necessary, how teaching and learning processes may need to be reimagined, how AI can be integrated to drive pedagogical innovation, and why such changes are essential when viewed in light of the foundational purpose of higher education. Ultimately, the editorial calls for intentional action to transform institutional structures and realign them with that original purpose, with AI serving as an enabler rather than a constraint.

AI is rapidly entering the scientific peer-review process, promising efficiency while quietly unsettling the intellectual foundations of scholarly judgment. The risk is not simply that AI will write flawed reviews but that scholars will begin writing for AI reviewers. Drawing on an empirical test of AI poisoning, this editorial shows how AI-generated reviews can misdirect manuscript development. Engineered manuscripts optimized for AI approval and hidden instructions designed to manipulate AI reviews threaten the knowledge production process. We argue that AI can assist scholarly work, but it cannot be a peer. Peer review is a human intellectual practice grounded in judgment, disciplinary experience, and the collective pursuit of knowledge. Bans and disclosure rules are insufficient. What is needed are purpose-built AI review platforms, reviewer education, and community governance that preserve human oversight while using AI responsibly.

Artificial intelligence (AI) agents have infiltrated most aspects of our lives and the technologies we use, including social media. Content (and moderation thereof) on many popular social media platforms is currently a mix of human- and AI-generated. Depending on many technical, situational, and contextual factors, both human- and AI-generated content on social media platforms have the potential to either fuel or fight the stigmatization of marginalized groups. Stigmatization is the social process of stereotyping, devaluing, or marginalizing individuals or groups based on certain characteristics such as gender, ethnicity, religion, or race, which can lead to social exclusion and psychological damage. The impact of AI agents on either reinforcing or reducing existing stigmatization on social media platforms remains largely unknown within our scholarly community. We have even less understanding of how to design and implement AI agents that can actively help transform social media platforms into spaces for combating stigmatization while empowering marginalized groups. In this editorial, we call on the information systems (IS) community to conduct empirical, design, and theoretical research at the intersection of social media, specific AI systems or agents, and marginalized contexts. Advancing research in this space is critical to understanding and shaping the future of social media platforms as a social good and as places to reduce stigmatization.

This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a strong centralizing force due to data and resource monopolization by large corporations, blockchain offers a counterbalancing mechanism through its inherent decentralization, transparency, and security. The editorial argues that these technologies are not mutually exclusive but possess complementary strengths. Blockchain can mitigate AI's centralizing risks by enabling decentralized data management, computation, and governance, promoting greater inclusivity, transparency, and user privacy. Conversely, AI can enhance blockchain's efficiency and security through automated smart contract management, content curation, and threat detection. The core argument calls for the development of ''decentralized intelligence'' (DI)—an interdisciplinary research area focused on creating intelligent systems that function without centralized control.

Organizations increasingly emphasize technical knowledge and analytical skills for their job candidates, but an overwhelming percentage of college students do not consider their analytical skills adequate. Moreover, gaps exist in digital skills between underserved students and their counterparts. This study examines the Excel skills learning of first-generation college students (FGCS) and their peers by focusing on FGCS motivation and perceived learning in a campus-wide Excel Skills Training Workshop at a four-year, minority-serving public university in the United States. Our regression analysis of 88 paired survey responses shows that FGCS were less likely than their peers to perceive a successful learning outcome, but individual motivation had a significantly positive effect on students’ perception of their Excel skills learning. Our supplemental analysis of 24 teams in the post-workshop case analysis competition reveals that students’ self-perception is inconsistent with their actual performance in some Excel skills, and students performed better in tasks that require technical skills than those requiring soft skills. Our study provides practical implications for designing scalable, effective analytics skills training programs in accounting and business education.

At the intersection of technology and marginalization lies an urgent need for research that not only interrogates these tensions but also reimagines new possibilities. To promote and grow research on technology and marginalized contexts, we launched a new invitedtrack, "IT, Social Justice, and Marginalized Contexts," at HICSS for three consecutive years (2024-2026); built a community of a total of 600+ scholars (submitting authors, reviewers, and/or minitrack chairs); co-wrote and published four editorials on technology and marginalization; and published HICSS special issues (including this current issue). In this editorial, we look back at our initial efforts, present our current progress, and provide a few directions for further research at the intersection of technology and marginalization.

The COVID-19 pandemic disrupted the delivery of education across the United States, giving rise to a myriad of challenges for university students. Yet, many students demonstrated tremendous resilience. Using an online survey conducted at a Hispanic-Serving Institution in May 2021, this study examines the relationship between coping strategies, learning barriers, and resilience among Public Administration students approximately one year into the pandemic while students were still taking their courses online. The regression analysis of 180 survey responses finds a positive and statistically significant correlation relationship between proactive coping strategies of Public Administration students and their resilience, while avoidant coping strategies and learning barriers are inversely correlated with student resilience. Moreover, proactive coping strategies lessened the negative effect of both learning barriers and avoidant coping on resilience. The findings imply that Public Administration programs should prioritize efforts to strengthen proactive coping strategies and reduce learning barriers to support student resilience and success.

The rise of the digitally enabled gig economy has prompted debate about gig working conditions and labor regulation, calling for research on the interactions between gig workers and digital labor platforms (DLPs). This study examines the working conditions on place-based and remote-work platforms by focusing on worker perspective and addressing two questions: What risks do workers perceive when engaging in different types of gig work? How is labor agency exercised on different digital labor platforms? To answer these research questions, we chose a comparative qualitative case-study research design and drew on labor agency theory to use the classification of resistance, resilience, and reworking as a starting point for categorizing labor agency actions on different DLPs. Narratives of workers through semi-structured surveys were collected from 102 California-based gig workers registered on three types of DLPs: delivery, ridesharing, and microtask crowdworking. Our thematic analysis shows that workers on the three types of DLPs shared four types of risk- employment, financial, mental health, and technological-but the frequencies of these risks differed across the platforms. Two risks-entrepreneurial and physical health risks-were primarily perceived by workers engaging in delivery and ridesharing. The study reveals that workers' enactment of the three types of labor agency (resistance, reworking, and resilience) varied by risk types and DLPs and provides a nuanced understanding of the gig work risks and worker agency across DLPs, extending labor agency theory from traditional workplaces to gig work environments.

The emergence of crowdsourcing as a new form of work has introduced a paradox among workers who receive small payments for piecemeal microwork yet continue to participate in microwork digital labor platforms (DLPs). To better understand what sustains microworkers’ participation, this study draws upon individual labor supply theory to quantitatively examine the impacts of microworkers’ motivations, perceptions, and preferences on their labor supply and wages. To explore the meaning of monetary rewards for microworkers, a qualitative inquiry explores microworkers’ spending patterns. Based on a survey of 306 microworkers on Amazon Mechanical Turk, our hierarchical regression analysis reveals that while individual motivations for monetary rewards, enjoyment, and microtime structure have some impact on the microwork labor supply and wages, their impact is limited. Our thematic analysis uncovers diverse meanings attached to microwork earnings. The two most noted are meeting subsistence needs and nonessential expenditures, both of which have positive effects on microwork wages. By investigating the elasticity of the microwork labor supply and wages and offering a nuanced understanding of monetary rewards, our study contributes to information management research on DLPs. Moreover, it provides practical insights for various stakeholders, including microworkers, requesters, and DLP operators.

Generative artificial intelligence (AI) represents a crucial subset of AI models characterized by their ability to generate new content based on user input, showing vast potential to transform learning and teaching. However, educators have raised ethical concerns, particularly regarding the adverse effect on students' learning if students simply parrot generative AI-generated content without engaging in critical analysis or original thought. Moreover, there exists the potential of generative AI to perpetuate existing biases in training data. This editorial discusses three major concerns in generative AI use in education and proposes questions (on task-AI fit and people-AI fit) and approaches to address the ethical considerations by adopting five principles of AI ethics. The editorial also discusses developing a classroom AI use policy as one governance mechanism for promoting ethical use of AI. As generative AI technology continues to evolve, so must our educational practices. The editorial ends with a call for readers (educators) to collaboratively define the terms of engagement with generative AI in educational settings and to begin this discourse by sharing insights and experiences with promoting ethical use of generative AI.

In an era where generative AI is reshaping the landscape of business and technology, this editorial addresses the critical imperative for transformative reform in business education. It emphasizes the dual nature of generative AI as both a formidable disruptor and a catalyst for innovation, necessitating a shift in how we educate the future workforce. The editorial calls for a proactive and comprehensive reevaluation of current educational models, advocating for an integration of AI literacy and ethical considerations into the core of business curricula. We aim to galvanize academia into action, advocating for an educational evolution that not only acknowledges the challenges posed by AI but also harnesses its potential to enrich and advance business education in preparing students for an AI-driven future.

Information systems (IS) scholars are gradually conducting research on socio-technical issues in marginalized contexts to uncover inequities and injustices and empower them through technology. In support of the evolving IS research landscape, a dedicated track on IT, social justice, and marginalized contexts was organized at HICSS 2024 that would allow the IS community to attract, promote, and grow research on marginalized contexts systematically and deliberately. This editorial presents a brief overview of the ten mini-tracks accepted as a part of this special HICSS 2024 track and highlights the prominent themes showcased in the 39 accepted papers. The editorial calls for IS scholars to challenge the hegemonic conduct of IS research and advance scholarship in this space by prioritizing intellectual conversations that mitigate the risks of constructing a future where technological spaces, digital applications, and machine intelligence mirror a narrow and privileged vision of society with its biases and stereotypes. The editorial concludes with a call to collectively seed a scholarly legacy in IT research within marginalized contexts at HICSS.