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426 papers
Results show that the informativeness and novelty of GAI outputs significantly and positively predict designers’ co-creation intentions, and actionable guidance for GAI application in design practice is provided.
This study examines the role of AI-powered chatbots in enhancing customer service within banking institutions by reviewing existing literature and proposing a conceptual framework for chatbot adoption, and highlights challenges associated with privacy, trust, cybersecurity, and ethical considerations.
This editorial highlights the evolving principles of responsible GAI use in publishing, including the prohibition of GAI authorship, disclosure of GAI assistance, and continued human oversight.
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Past literature on face recognition for monozygotic (("identical") twins points to facial marks and mirror asymmetry as possible directions for improved accuracy of twins recognition. The Celeb Twins Test Set (CTTS) contains web-scraped image pairs for 80 sets of celebrity twins. It is the only twins test set with meta-data for twins with distinguishing skin marks and possible mirror asymmetry. CTTS is organized in the manner of face verification test sets such as LFW, CALFW, CPLFW, CFP-FP, and AgeDB-30. Current deep CNN matchers can achieve over 76% accuracy in classifying CTTS same-person / different-person image pairs. We show that current matchers do not make use of skin marks, or asymmetry, and discuss reasons for this. Finally, we discuss the feasibility of using generative AI tools such as Grok, ChatGPT and Gemini to create images of imagined monozygotic twins as a means to increase representation of twins in face recognition training sets.
Recent advances in generative AI are raising ethical concerns regarding the originality of generated content and the potential replication of training data, with further implications for transparency, attribution, and intellectual property. In music, several computational approaches have been proposed to identify potential replication, using audio-based similarity metrics. Yet, their alignment with human judgments across distinct musical attributes remains underexplored. To address this gap, we conduct a perceptual experiment on music matches, defined as strongly similar musical excerpts. We focus on five musical attributes: melody, harmony, rhythm, voice, and timbre. We design a triplet-based forced-choice task comprising 300 cases, including plagiarism examples, cover songs, and AI-generated music. From this experiment, we introduce the MATCHA (Musical Attribute-based Triplet Comparison with Human Annotations) dataset: a collection of 1105 perceptual assessments of attribute-based music matches from 83 expert participants. Our findings reveal measurable agreement among participants in identifying matches across attributes. We further observe partial alignment between human judgments and computational similarity measures. Overall, this work underscores the importance of domain-specific and perceptually grounded evaluation frameworks for generative AI in creative practice.
Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative AI is fundamentally constrained by the limited, time-varying onboard energy supplied by solar \emph{energy harvesting} (EH). This paper presents a framework for solar-powered space generative AI in which a satellite receives a user prompt, executes a diffusion-based image-generation model, and downlinks the compressed result within a strict time window. We identify the fundamental \emph{computation--communication} (C$^2$) trade-offs governed by the shared harvested-energy budgets. Specifically, increasing the number of generation steps improves intrinsic image quality but depletes energy and time available for downlink transmission, whereas prioritizing communication guarantees reliable delivery but sacrifices semantic quality. To balance these trade-offs and maximize \emph{end-to-end} (E2E) generative performance, we exploit the predictable solar-EH dynamics induced by deterministic orbital motion and develop a joint C$^2$ resource-optimization framework using a tractable two-step approach. First, we characterize the maximum downlink throughput for a fixed generation depth under continuous solar EH. This establishes a separation principle that decouples waiting-time selection from optimal transmit-power control. Next, we formulate a joint C$^2$ utility-maximization problem and derive a closed-form, low-complexity step-selection policy in the dominant constant-power regime. Extensive experiments under realistic orbital dynamics demonstrate that the proposed policy dynamically balances generation quality and transmission reliability. This yields significant E2E performance gains over static computation- and communication-centric baselines across diverse solar-EH states.
Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains underexplored. Prior work includes openness-focused evaluation frameworks, such as MusGO (Music-Generative Open AI), as well as qualitative studies of musicians'experiences with generative systems. However, these approaches do not support systematic comparison or early-stage discovery of models for creative use. Motivated by such limitations, we introduce MusGU+, a musician-centered evaluation framework organized around three dimensions: Adaptability, Usability, and Controllability. Together, these capture whether a model can be feasibly trained or fine-tuned on personal data, integrated into real-world music workflows, and controlled in musically meaningful ways. We evaluate 10 representative generative music systems and present an interactive discovery tool that enables musicians to explore and filter models according to these criteria. While MusGO remains valuable for promoting responsible research practices, MusGU+ supports informed selection and practical adoption of generative systems by musicians.
The spread of generative artificial intelligence (AI) in vocational classrooms has raised a concern that easy, on-demand answers may displace the reasoning vocational graduates are expected to master. This cross-sectional survey examined whether students' perceptions of AI are associated with their self-reported critical thinking, and whether self-regulated learning accounts for any such association. Drawing on social cognitive theory and the Technology Acceptance Model, questionnaire data were collected from 365 students in nine state vocational schools (SMK) in Pesisir Selatan Regency, West Sumatra, and analysed with PLS-SEM. All four constructs, including critical thinking, were measured by self-report rather than by performance tasks. Perceived usefulness and perceived ease of use were not associated with critical thinking directly (β = -.026, 95% CI [-.163, .111]; β = .064, 95% CI [-.072, .200]) but were weakly associated with self-regulated learning (β = .161; β = .158), which was in turn the strongest correlate of critical thinking (β = .383, 95% CI [.284, .482]). Both indirect paths were small and positive (β = .062 and .061), a configuration indicating indirect-only mediation. Explanatory power was weak, with R² = .085 for self-regulated learning and .157 for critical thinking, and f² values for all four technology paths below .02. A clustering sensitivity analysis showed that the technology paths lose significance at an intraclass correlation above about .01, so these associations are provisional. Because all constructs were measured on one occasion, the ordering is a theoretical assumption rather than an observed sequence.
AI-supported deliberation platforms increasingly support large-scale participation in public policymaking. Yet their design reflects strong rationalistic and emotion reductionist bias: while they structure arguments, cluster opinions, visualize disagreement or organize discussions, they largely ignore the emotional dynamics through which participants interpret claims, react to disagreement and sustain engagement. This blind spot is particularly problematic in discussions surrounding wicked public issues, where emotions are not peripheral but integral to how actors evaluate arguments and orient themselves toward collective consensus-oriented decisions. At the same time, recent advances in Generative Artificial Intelligence (GenAI) introduce new possibilities for interacting with emotional signals in digital communication. Beyond content processing, GenAI systems can detect, interpret, and generate context-sensitive emotional expressions; this opens the possibility of GenAI-mediated emotional support in deliberative environments. This study explores how such systems could be designed. Following an echeloned Design Science Research approach, the paper focuses on the initial stages of a broader design project aimed at developing a GenAI mediator for consensus-oriented online deliberation. The problem space is examined through an analysis of 3 existing deliberation platforms and a semisystematic literature review of 25 papers on emotional dynamics in consensus-oriented discussions. Using a three-step thematic synthesis, the study derives design knowledge, articulated through design requirements, for emotion-aware GenAI mediation. The results of this study consist of two outcomes. First, the analysis formulates a problem statement that identifies a gap in current deliberation platforms: while they organize informational exchanges, emotional dynamics remain unmodeled. Second, the study derives six design requirements that define the design space a solution must satisfy: emotional awareness, emotional regulation, emotional inclusive design, emotional stability, emotional conflict transformation, and emotional articulation. These findings contribute to an initial body of design knowledge that bridges research on emotions in deliberation with emerging capabilities of GenAI; they lay the groundwork for designing emotion-aware GenAI-mediated deliberations.
This study investigates how privacy has been conceptualized across technical, organizational, behavioral, ethical and governance perspectives and identifies key gaps and emerging challenges within the literature and contributes to the development of a more comprehensive perspective on privacy as a multidimensional issue.
This work examines the early use of an in-house generative AI health assistant designed to predraft documentation and streamline communication and explores the integration of electroencephalography data captured through brain-computer interface devices such as Galea and EMOTIV headsets.
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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Gemini Omni 1.1 Flash brings a new suite of creative controls and generative video capabilities to developers.
