Tootfinder

Training for Obsolescence? The AI-Driven Education Trap
Andrew J. Peterson
https://arxiv.org/abs/2508.19625 https://arxiv.org/pdf/2508.19625

Training for Obsolescence? The AI-Driven Education Trap
Artificial intelligence simultaneously transforms human capital production in schools and its demand in labor markets. Analyzing these effects in isolation can lead to a significant misallocation of educational resources. We model an educational planner whose decision to adopt AI is driven by its teaching productivity, failing to internalize AI's future wage-suppressing effect on those same skills. Our core assumption, motivated by a pilot survey, is that there is a positive correlation between…

Generation and Evaluation in the Human Invention Process through the Lens of Game Design
Katherine M. Collins, Graham Todd, Cedegao E. Zhang, Adrian Weller, Julian Togelius, Junyi Chu, Lionel Wong, Thomas L. Griffiths, Joshua B. Tenenbaum
https://arxiv.org/abs/2508.10914

Generation and Evaluation in the Human Invention Process through the Lens of Game Design
The human ability to learn rules and solve problems has been a central concern of cognitive science research since the field's earliest days. But we do not just follow rules and solve problems given to us by others: we modify those rules, create new problems, and set new goals and tasks for ourselves and others. Arguably, even more than rule following and problem solving, human intelligence is about creatively breaking and stretching the rules, changing the game, and inventing new problems wort…

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Milena Davudova, Ziyuan Cai, Valentina Giunchiglia, Dragos C. Gruia, Giulia Sanguedolce, Adam Hampshire, Fatemeh Geranmayeh
https://arxiv.org/abs/2507.17326

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Detailed assessment of language impairment following stroke remains a cognitively complex and clinician-intensive task, limiting timely and scalable diagnosis. Automatic Speech Recognition (ASR) foundation models offer a promising pathway to augment human evaluation through intelligent systems, but their effectiveness in the context of speech and language impairment remains uncertain. In this study, we evaluate whether Whisper, a state-of-the-art ASR foundation model, can be applied to transcri…

Machine Mirages: Defining the Undefined
Hamidou Tembine
https://arxiv.org/abs/2506.13990 https://arxiv.org/pdf/2506.13990

Machine Mirages: Defining the Undefined
As multimodal machine intelligence systems started achieving average animal-level and average human-level fluency in many measurable tasks in processing images, language, and sound, they began to exhibit a new class of cognitive aberrations: machine mirages. These include delusion, illusion, confabulation, hallucination, misattribution error, semantic drift, semantic compression, exaggeration, causal inference failure, uncanny valley of perception, bluffing-patter-bullshitting, cognitive stereo…

Generative AI in Training and Coaching: Redefining the Design Process of Learning Materials
Alexander Komar, Marc-Andr\'e Heidelmann, Kristina Schaaff
https://arxiv.org/abs/2508.11662

Generative AI in Training and Coaching: Redefining the Design Process of Learning Materials
Generative artificial intelligence (GenAI) is transforming education, redefining the role of trainers and coaches in learning environments. In our study, we explore how AI integrates into the design process of learning materials, assessing its impact on efficiency, pedagogical quality, and the evolving role of human trainers and coaches. Through qualitative interviews with professionals in education and corporate training, we identify the following key topics: trainers and coaches increasingly …

$NavA^3$: Understanding Any Instruction, Navigating Anywhere, Finding Anything
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang, Wenbo Ding, Shanghang Zhang
https://arxiv.org/abs/2508.04598

$NavA^3$: Understanding Any Instruction, Navigating Anywhere, Finding Anything
Embodied navigation is a fundamental capability of embodied intelligence, enabling robots to move and interact within physical environments. However, existing navigation tasks primarily focus on predefined object navigation or instruction following, which significantly differs from human needs in real-world scenarios involving complex, open-ended scenes. To bridge this gap, we introduce a challenging long-horizon navigation task that requires understanding high-level human instructions and perf…

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
Vivek Chavan, Arsen Cenaj, Shuyuan Shen, Ariane Bar, Srishti Binwani, Tommaso Del Becaro, Marius Funk, Lynn Greschner, Roberto Hung, Stina Klein, Romina Kleiner, Stefanie Krause, Sylwia Olbrych, Vishvapalsinhji Parmar, Jaleh Sarafraz, Daria Soroko, Daksitha Withanage Don, Chang Zhou, Hoang Thuy Duong Vu, Parastoo Semnani, Daniel Weinhardt, Elisabeth Andre, J\"org Kr\"uger, Xavier Fresquet

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI systems that simulate or interpret human emotions are reshaping our interactions in areas such as education, healthcare, mental health, caregiving, and digital life. The analysis is structured around four central themes: the ethical implications of emotional AI, …

Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Chengyuan Zhang, Cathy Wu, Lijun Sun
https://arxiv.org/abs/2506.14762

Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Accurate and interpretable car-following models are essential for traffic simulation and autonomous vehicle development. However, classical models like the Intelligent Driver Model (IDM) are fundamentally limited by their parsimonious and single-regime structure. They fail to capture the multi-modal nature of human driving, where a single driving state (e.g., speed, relative speed, and gap) can elicit many different driver actions. This forces the model to average across distinct behaviors, red…

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A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy
This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-loop decision making. Existing frameworks in SOCs often focus narrowly on automation, lacking systematic structures to manage human oversight, trust calibration, and scalable autonomy with AI. Many assume static or binary autonomy settings, failing to account for the varied complexity, criticality, and risk across SOC tasks c…

Feeling Guilty Being a c(ai)borg: Navigating the Tensions Between Guilt and Empowerment in AI Use
Konstantin Aal, Tanja Aal, Vasil Navumau, David Unbehaun, Claudia M\"uller, Volker Wulf, Sarah R\"uller
https://arxiv.org/abs/2506.00094

Feeling Guilty Being a c(ai)borg: Navigating the Tensions Between Guilt and Empowerment in AI Use
This paper explores the emotional, ethical and practical dimensions of integrating Artificial Intelligence (AI) into personal and professional workflows, focusing on the concept of feeling guilty as a 'c(ai)borg' - a human augmented by AI. Inspired by Donna Haraway's Cyborg Manifesto, the study explores how AI challenges traditional notions of creativity, originality and intellectual labour. Using an autoethnographic approach, the authors reflect on their year-long experiences with AI tools, re…