An empirical study on leveraging generative artificial intelligence in promoting inquiry-based learning

Purpose This meta-analysis aims to comprehensively review the impact of Generative Artificial Intelligence (Gen-AI) on college students’ critical thinking (CT) by quantitatively integrating the results of relevant empirical studies to obtain the overall effect. Design/methodology/approach This meta-analysis synthesized data from 39 empirical studies published between 2023 and 2025. Effect sizes were calculated using random-effects models, and moderator analyses were conducted to examine potential influencing factors, including Gen-AI literacy level, disciplines, knowledge types, pedagogical approaches, user roles, Gen-AI interface types, Gen-AI roles, and Gen-AI task types. Findings The results indicated that Gen-AI had a moderately positive effect on CT (g = 0.591). Further analysis identified five significant moderating variables: disciplines, knowledge types, pedagogical approaches, Gen-AI roles and Gen-AI task types. Specifically, Gen-AI has the greatest positive impact on college students’ CT in STEM, procedural knowledge, inquiry-based learning, as a peer, and in the context of performing reflective and metacognitive tasks. These results suggest that within the overall contribution range of Gen-AI to college students’ CT, in some cases they may be more effective. Originality/value Previous research reviews, when exploring students’ higher-order thinking, did not make a clear distinction among the different types of thinking within them. Therefore, it is necessary to separate CT from broad learning outcomes or higher-order thinking and analyze its relationship with Gen-AI separately.

Purpose:Artificial Intelligence (AI) has long played a crucial role in language education, and the emergence of Generative AI (GAI) is set to further enhance this impact. However, comprehensive reviews of high-quality empirical research on GAI in language education are lacking. Design: To fill this gap, the current study analyzed 43 empirical studies published in SSCI journals from 2022 to 2024, focusing on contextual, technological, theoretical characteristics as well as research objectives. Findings and Originality: The findings reveal both progress and ongoing challenges in GAI research. Contextually, most of the selected studies concentrated on English language teaching and learning, particularly among Chinese learners and teachers in higher education, with a strong emphasis on writing skills. Technologically, previous studies exhibited a notable overreliance on ChatGPT, insufficient details on GAI versions, and inadequate reporting of GAI prompts. Theoretically, 46.5% of the selected studies did not specify a clear framework, while those that did mainly drew from psychological, technological, and social theories. As for research objectives, 41.9% of the selected studies examined users' perceptions, while the rest evaluated AI impact, explored AI practice, and compared AI performance against other tools.This review concludes with six key recommendations to advance GAI research in language education: (1) Expand participant diversity; (2) Explore AI models beyond ChatGPT; (3) Examine prompt patterns to enhance AI literacy; (4) Integrate theoretical frameworks in GAI research; (5) Investigate cross-validation skills for enhanced critical thinking; (6) Leverage GAI's multimodal capabilities to explore language skills beyond writing.

Generative Artificial Intelligence (GenAI) has been widely integrated into second language (L2) learning, but studies concerning how L2 learners interact with GenAI are still in early stages, and there is also a lack of a review of existing studies.This study systematically reviews the existing literature on empirical studies in GenAI-assisted translation via a four-dimensional engagement framework: cognitive, behavioural, emotional, and agentic engagement, aiming to define the manifestations of the above-mentioned four dimensions of engagement with GenAI in translation and probing for probable factors contributing to changes in engagement. Cognitively, GenAI facilitates learners to stimulate deeper insights into the text and to allocate resources while translating. Behavioural engagement differs according to language proficiency. Translation students use GenAI as compensation, whereas professionals use it as optimization. The positive or negative characteristic of emotional engagement is largely decided by whether GenAI is used as an assistant or a substitute. Learners demonstrate awareness of the potential drawbacks and risks of GenAI as a representation of agentic engagement. Yet only a few follow up with the exploration of their agentic actions. This review offers a unified analytical framework for the following section of analysis of learners' engagement with GenAI in translation. It also points out the implications for more empirical studies on learners' agentic actions, taking translation types into consideration.

The rise of artificial intelligence (AI) and, more recently, generative AI (GAI) has transformed digital marketing, particularly within social media. However, academic research on this intersection remains dispersed, requiring a structured synthesis to identify prevailing trends and gaps. Given the increasing integration of AI in digital marketing, understanding its implications for consumer behaviour is crucial for both researchers and practitioners. This study conducts a systematic literature review (SLR) following the SPAR‐4‐SLR protocol to analyse existing research on AI, GAI, social media, and consumer behaviour. In addition, the 5W1H framework is used to organise information and answer questions that arise. Specifically, it examines how AI is portrayed in social media and consumer behaviour literature, whether as an enabler, risk, or neutral factor, the perspective taken by the studies, and the application given to it. Findings show that AI is primarily framed as a driver of personalisation, engagement, and analytics, yet notable concerns about ethical risks like algorithmic bias and privacy persist. Research perspectives vary, spanning consumer, business, and integrative views that reflect the complex AI influence on user experience and organisational strategy. Empirical studies mainly treat AI as a core subject, focusing on applications such as chatbots, recommendation systems, and virtual influencers (VIs). A smaller number employ AI methodologically for social media data analysis through machine learning (ML) and natural language processing (NLP). Despite growth, significant gaps remain in understanding AI's long‐term effects, cross‐cultural nuances, and theoretical integration. Ethical issues highlight the need for responsible AI frameworks balancing innovation and fairness. This review synthesises current knowledge and outlines future research directions, aiming to guide academic inquiry and responsible implementation of AI in digital consumer contexts.

The emergence of generative artificial intelligence (GenAI) is reshaping the research landscape and carries significant implications for Digital Humanities (DH), a field long intertwined with computational methods and technologies. This study examines how DH scholars are adopting and critically evaluating GenAI in their research. Drawing on an international survey of 76 respondents and 15 in‐depth interviews, we investigate scholars' motivations for using GenAI tools, the specific practices through which they integrate these tools into their research, and their perceptions of the benefits, risks, and challenges associated with GenAI. Our findings reveal divergent opinions and imaginaries within the DH community: while many scholars view GenAI as a means to enhance efficiency and support reskilling, others express concern about its impact on scholarly identity, intellectual labor, and disciplinary values. Situated within the history of DH and analyzed through the lens of actor‐network theory, the results suggest that GenAI is being incrementally enrolled into DH research networks, reshaping relationships among human and nonhuman actors in ways that remain contested and actively negotiated. As one of the first empirical studies on this topic, this work provides an initial foundation for understanding GenAI's evolving role in DH scholarship and points toward avenues for future research.

Implementing generative AI (GenAI) in higher education: A systematic review of case studies

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

The study, conducted in 2025, investigated the digital readiness and use of generative artificial intelligence (AI) among small-scale farmers in Nigeria. A multi-stage sampling technique was used to select 120 small-scale farmers, and data were collected through interview schedules. The majority (62.5%) were small-scale farmers with over ten years of farming experience. Many of the small-scale farmers had digital access as a lot of them owned smart phones (64.2%) had internet connectivity (65%), and regularly used the internet (53.3%). Traditional media (Radio and TV) (63.3%) remained their primary source of agricultural information. Extension service access (4.2%) was notably low. Many small scale farmers (64.2%) had used generative AI, mainly for accessing information (45%) and conducting basic research about their farm operations and general well-being (17.5%), and most indicated willingness to continue its use (89.2%). However, major barriers to the use of generative AI included limited awareness and lack of access to digital devices. AI awareness was generally low but positively associated with education. Although generative AI adoption is growing, significant challenges remain, underscoring the need for targeted generative AI training in agriculture as well as the design and implementation of more generative AI awareness program.

The educational landscape is quickly evolving, presenting many opportunities. At the same time, there are tests to be passed when Generative Artificial Intelligence (AI) comes into the picture. Better rides going ways to alter education to enroll in the AI era, worth of the best integration of Generative AI technologies. To start, our deliberation will open discussions on how generative AI can precipitate authentic revolutions in the enhancement of learning experiences, customized tutorials, and generating very different contextualization. We continue to explore evils to the integration of AI that has cropped up as a result; issues of ethics, privacy, and educator training, all stand as major adversaries in this context. Therefore, our theoretical proposal is drawn from literature and empirical studies. It offers a structure by which lecturers or schools may integrate Generative AI effectively. The framework pertains to curriculum realignment, teacher training programs, augmented infrastructure, and a robustly piloted/code of ethics. We are further provoked to encourage and improve collaboration among scholars, technologists, policymakers, and stakeholders about ensuring the conscientious and ethical use of AI in educational settings. It is an asset valuable for educators, users, and policy developers keen on inserting the energy of Generative AI into the consistently disorderly order of the AI era. Cutting-edge methodologies and inclusive of a culture of adaptive change, education can now truly flourish in a world increasingly shaped by AI, supported by modern-day learners and teachers in the twenty-first century and beyond.

Recently, there has been growing research interest in the integration of generative artificial intelligence (GenAI) in educational contexts, particularly in academic writing. In multilingual contexts where students struggle with the complexities of academic writing, particularly at the graduate level, the adoption of GenAI may play a critical role in supporting graduate academic writing. This study aimed to conduct a scoping review to determine the benefits, challenges, concerns, and research gaps associated with GenAI adoption in L2 graduate academic writing within higher education. Articles that described the adoption of GenAI in L2 Graduate academic writing were searched across four databases: Scopus, Web of Science, Google Scholar, and EBSCOhost. Articles that were not specific to L2 graduate academic writing, not empirical studies and not written in English were excluded. Eight empirical studies (2024-2025) that focused on L2 graduate academic writing were selected for the review. The results revealed that all eight studies reported notable improvements in students’ academic writing, including enhanced grammar, spelling, coherence, and writing style. Tools such as Grammarly and ChatGPT were found to be particularly beneficial for non?native English?speaking graduate students. However, the review also identified key challenges including ethical concerns and the risk of over-reliance on GenAI-generated content. Overall, the review concludes that while GenAI tools show strong potential for enhancing L2 graduate academic writing skills, further research and policy development are needed to guide responsible and effective integration of GenAI within universities.

To date, innovation management research on idea evaluation has focused on human experts and crowd evaluators. With recent advances in artificial intelligence (AI), idea evaluation and selection processes need to keep up. As a result, the potential role of AI‐enabled systems in idea evaluation has become an important topic in innovation management research and practice. While AI can help overcome human capacity constraints and biases, prior research has identified also aversive behaviors of humans toward AI. However, research has also shown lay people's appreciation of AI. This study focuses on human crowdvoters’ AI adoption behavior. More precisely, we focus on gig workers, who despite often lacking expert knowledge are frequently engaged in crowdvoting. To investigate crowdvoters' AI adoption behavior, we conducted a behavioral experimental study (n = 629) with incentive‐compatible rewards in a human‐AI augmentation scenario. The participants had to predict the success or failure of crowd‐generated ideas. In multiple rounds, participants could opt to delegate their decisions to an AI‐enabled system or to make their own evaluations. Our findings contribute to the innovation management literature on open innovation, more specifically crowdvoting, by observing how human crowdvoters engage with AI. In addition to showing that the lay status of gig workers does not lead to an appreciation of AI, we identify factors that foster AI adoption in this specific innovation context. We hereby find mixed support for influencing factors previously identified in other contexts, including financial incentives, social incentives, and the provision of information about AI‐enabled system's functionality. A second novel contribution of our empirical study is, however, the fading of crowdvoters’ aversive behavior over time.

Introduction: The integration of Artificial Intelligence (AI) in medical education has emerged as a transformative shift, particularly within Competency-Based Medical Education (CBME). AI technologies, including Natural Language Processing (NLP) and machine learning, offer opportunities to enhance personalized learning and competency assessment. Methods: A scoping review was conducted following the framework by Arksey and O'Malley (2005) to examine the current integration of AI in CBME. Empirical studies were included, focusing on AI applications in medical education, competency assessments, and skill development. Results: The 50 studies, published from 2010 to 2025, were included in the scoping review and the synthesized evidence demonstrated that AI has shown potential in automating assessments, providing real-time feedback, and supporting personalized learning paths. Common AI technologies such as generative AI, NLP, and machine learning were applied across diverse medical education settings. However, challenges regarding ethical concerns, faculty training, and limited integration within established curricula were identified. Conclusion: The integration of AI into CBME offers significant potential in medical education; however, several challenges remain. There is a need for more empirical research, longitudinal studies, and AI literacy programs such as training in prompt engineering, AI ethics, and responsible data use for both educators and students. Addressing these gaps will ensure AI’s effective, ethical, and equitable integration in medical training.

Purpose This study aims to critically examine the socio-technical, economic and governance challenges emerging at the intersection of Generative artificial intelligence (AI) and Urban AI. By foregrounding the metaphor of “the moon and the ghetto” (Nelson, 1977, 2011), the issue invites contributions that interrogate the gap between technological capability and institutional justice. The purpose is to foster a multidisciplinary dialogue–spanning applied economics, public policy, AI ethics and urban governance – that can inform trustworthy, inclusive and democratically grounded AI practices. Contributors are encouraged to explore not just what GenAI can do, but for whom, how and with what consequences. Design/methodology/approach This study draws upon interdisciplinary literature from public policy, innovation studies, digital governance and urban sociology to frame the emerging governance challenges of Generative AI and Urban AI. It builds a conceptual foundation by synthesizing insights from comparative city case studies, innovation systems theory and normative policy frameworks. The approach is interpretive and exploratory, aiming to situate AI technologies within broader institutional, geopolitical and socio-economic contexts. The study invites contributions that adopt empirical, theoretical or practice-based methodologies addressing the governance of GenAI in cities and regions. Findings This study identifies a critical gap between the rapid technological advancements in Generative AI and the institutional readiness of public governance systems – particularly in urban contexts. It finds that current policy frameworks often prioritize efficiency and innovationism over democratic legitimacy, civic trust and inclusive design. Drawing on comparative global city experiences, it highlights the risk of reinforcing power asymmetries without robust accountability mechanisms. The analysis suggests that trustworthy AI is not a purely technical attribute but a political and institutional achievement, requiring participatory governance architectures and innovation systems grounded in public value and civic engagement. Research limitations/implications As an editorial introduction, this study does not present original empirical data but synthesizes key theoretical frameworks, case studies and policy debates to guide future research. Its analytical scope is conceptual and comparative, offering a foundation for submissions that further investigate Generative and Urban AI through empirical, normative and practice-based lenses. The limitations lie in its broad coverage and reliance on secondary sources. Nonetheless, it provides an agenda-setting contribution by highlighting the urgent need for interdisciplinary research into how AI reshapes public governance, institutional legitimacy and urban democratic futures. Practical implications This editorial offers a structured framework for policymakers, urban planners, technologists and public administrators to critically assess the governance of Generative and Urban AI systems. By highlighting international case studies and conceptual tools – such as public algorithmic infrastructures, civic trust frameworks and anticipatory governance – the article underscores the importance of institutional design, regulatory foresight and civic engagement. It invites practitioners to shift from techno-solutionist approaches toward inclusive, democratic and place-based AI governance. The reflections aim to support the development of trustworthy AI policies that are grounded in legitimacy, accountability and societal needs, particularly in urban and regional contexts. Social implications The editorial underscores that Generative and Urban AI systems are not socially neutral but carry significant implications for equity, representation and democratic legitimacy. These technologies risk reinforcing existing social hierarchies and systemic biases if not governed inclusively. This study calls for reimagining trust not as a technical feature but as a relational, contested dynamic between institutions and citizens. It encourages submissions that examine how AI reshapes the urban social contract, affects marginalized communities and challenges existing civic infrastructures. The goal is to promote AI governance frameworks that are pluralistic, just and reflective of diverse societal values and lived experiences. Originality/value This editorial offers a timely and conceptually grounded intervention into the emerging field of Urban AI and Generative AI governance. By framing the challenges through Richard R. Nelson’s metaphor of The Moon and the Ghetto, this study foregrounds the gap between technical capabilities and enduring societal injustices. The contribution lies in its interdisciplinary synthesis – bridging innovation systems, AI ethics, public policy and urban governance. It introduces a critical framework for assessing “trustworthy AI” not as a technical goal but as a democratic achievement and encourages research that is policy-relevant, equity-oriented and attuned to the institutional realities of AI in cities.

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

With the advent of artificial intelligence (AI) based systems, a new era has begun. Decisions that were once made by humans are now increasingly being made by these advanced systems, with the inevitable consequence of our growing reliance on AI in many aspects of our lives. At the same time, the opaque nature of AI-based systems and the possibility of unintentional or hidden discriminatory practices and biases raises profound questions not only about the mechanics of AI, but also about how users perceive the fairness of these systems. We hypothesize that providing various explanations for AI decision-making processes and output may enhance users’ fairness perceptions and make them trust the system and adopt its decisions. Hence, we devised an online between-subject experiment that explores users’ fairness and comprehension perceptions of AI systems with respect to the explanations provided by the system, employing a case study of a managerial decision in the human resources (HR) domain. We manipulated (i) the decision-maker (AI or human); (ii) the input (candidate characteristics); (iii) the output (recommendation valence), and (iv) the explanation style. We examined the effect of the various manipulations (and individuals’ demographic and personality characteristics) using multivariate ordinal regression. We also performed a multi-level analysis of experiment components to examine the effects of the decision-maker type, explanation style, and their combination. The results suggest three main conclusions. The first conclusion is that there is a gap in users’ fairness and comprehension perception of AI-based decision making systems compared to human decision making. The second conclusion is that knowing that an AI-based system provided the decisions negatively affects users’ fairness and comprehension perceptions, compared to knowing that humans made the decision. Finally, the third conclusion is that providing case-based, certification-based, or sensitivity-based explanations can narrow this gap and may even eliminate it. Additionally, we found that users’ fairness and comprehension perceptions are influenced by a variety of factors such as the input, output, and explanation provided by the system, as well as by individuals’ age, education, computer skills, and personality. Our findings may help to understand when and how to use explanations to improve users’ perceptions regarding AI-based decision-making. CCS CONCEPTS • Human computer interaction (HCI) → HCI design and evaluation methods → User studies • Human-centered computing → Human computer interaction (HCI) → Empirical studies in HCI • Applied computing → Law, social and behavioral sciences → Sociology