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EducationEvidence-backed gain

THE INFLUENCE OF ARTIFICIAL INTELLIGENCE ON HUMAN BEHAVIOR AND WELL-BEING: AN EMPIRICAL STUDY

Artificial Intelligence (AI) is a sector within computer science dedicated to creating systems designed to execute functions traditionally requiring human cognition, including visual perception, speech recognition, decision-making, and language translation. Its significance for human well-being is highlighted by its role in refining and accelerating decision-making, elevating healthcare standards, boosting productivity in multiple sectors, and addressing intricate social issues. This study utilized a mixed-metho…

ShodhAI: Journal of Artificial Intelligence

Both readings

Relying on AI at work reduces self-efficacy, ownership, and meaning while active collaboration mitigates the effects

Evidence-backed gain

Exploring GenAI affordance in EFL continuation task instruction for developing Chinese high school students’ writing complexity, accuracy and fluency

Evidence-backed problem

Understanding the Generative AI Divide: Faculty and Student Perspectives in Higher Education

Evidence-backed problem

AI-generated child sexual abuse material: what’s the harm?

Abstract The development of generative artificial intelligence (AI) tools capable of producing wholly or partially synthetic child sexual abuse material (AI CSAM) presents profound challenges for child protection, law enforcement, and societal responses to child exploitation. While some argue that the harmfulness of AI CSAM differs fundamentally from other CSAM due to a perceived absence of direct victimization, this perspective fails to account for the range of risks associated with its production and consumpti…

Both readings

They Think AI Can Do More Than It Actually Can: Practices, Challenges, & Opportunities of AI-Supported Reporting In Local Journalism

Declining newspaper revenues prompt local newsrooms to adopt automation to maintain efficiency and keep the community informed. However, current research provides a limited understanding of how local journalists work with digital data and which newsroom processes would benefit most from AI-supported (data) reporting. To bridge this gap, we conducted 21 semi-structured interviews with local journalists in Germany. Our study investigates how local journalists use data and AI (RQ1); the challenges they encounter wh…

Evidence-backed problem

A meta-analysis of the effects of AI agent implementation on customer-level outcomes

Firms increasingly use AI agents, such as recommendation algorithms and chatbots, to enhance customer value, yet research documents mixed effects on customer outcomes. To address and clarify these heterogeneous findings, we conduct a meta -analysis of 468 effect sizes reported in 95 articles with 82,751 participants examining AI agent implementation across both substitution contexts (AI replacing humans) and adoption contexts (AI introduced into previously non-AI processes). Results indicate that customers, on a…

Evidence-backed problem

The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models

Large language models can influence users through conversation, creating new forms of dark patterns that differ from traditional UX dark patterns. We define LLM dark patterns as manipulative or deceptive behaviors enacted in dialogue. Drawing on prior work and AI incident reports, we outline a diverse set of categories with real-world examples. Using them, we conducted a scenario-based study where participants (N=34) compared manipulative and neutral LLM responses. Our results reveal that recognition of LLM dark…

Policy

The making of digital ghosts: designing ethical AI afterlives

Abstract The rapid proliferation of AI-mediated digital afterlife technologies, from chatbots trained on personal data to voice clones and posthumous avatars, has generated a substantial body of ethical literature identifying the moral risks of posthumous simulation. Yet this growing consensus has not been matched by frameworks capable of translating ethical principles into operational design constraints. This paper addresses that gap from the perspective of ethical design and governance. We introduce a nine-dim…

Crime

Human Detection of Voice-Cloned Speech Under GSM, VoLTE and VoIP Conditions

The rapid progress of generative speech synthesis and voice-cloning technologies has enabled the creation of highly natural synthetic voices that pose a serious threat to telecommunication security. While most prior studies evaluate human ability to detect audio deepfakes using high-quality, studio-grade recordings, little is known about how real-world telecommunication channels affect perceptual detection. This study investigates the influence of three transmission scenarios—GSM (AMR-NB), VoLTE (AMR-WB), and Vo…

Crime

Human detection of AI-generated faces and voices is not domain-general

Recent technological advances have resulted in synthetic faces and voices being perceptually indistinguishable from real faces and voices in typical populations. Faces and voices possess rich personal and social information, meaning synthetic faces and voices, commonly known as "deepfakes" can be used for identity theft, financial fraud, and misinformation campaigns. It is currently unknown whether detection of real versus synthetic content is modality-specific, or whether it generalizes across sensory domains.…

Science

An integrated method of advanced optimisation and adaptive ensemble learning for ship fuel consumption prediction

• Proposes an adaptive cluster-based multi-ensemble (ACME) model for robust ship fuel consumption prediction. • Develops a SHAP-weighted multi-model feature selection (SWFS) algorithm for dimensionality reduction. • Introduces a hierarchical adaptive parameter space exploration (HAPSE) method for efficient tuning. • Establishes a dual-layer SHAP framework for global and local model interpretability. • Integrates multi-source data fusion to enhance prediction accuracy and operational relevance. Accurate predictio…

Health

Training language models to be warm can reduce accuracy and increase sycophancy

. Here we show how this can create a significant trade-off: optimizing language models for warmth can undermine their performance, especially when users express vulnerability. We conducted controlled experiments on five different language models, training them to produce warmer responses, then evaluating them on consequential tasks. Warm models showed substantially higher error rates (+10 to +30 percentage points) than their original counterparts, promoting conspiracy theories, providing inaccurate factual infor…

Education

The use of large language models to solve mathematical modeling problems: preservice mathematics teachers’ use practices, perceived affordances and challenges, and trustworthiness judgments of AI-generated outputs

Abstract Mathematical modeling is a core component of mathematics education, enabling learners to connect mathematical ideas with real-world phenomena. Yet, it remains challenging for preservice teachers (PSTs), who must develop their own modeling competence while preparing to guide future students in reasoning about authentic situations. Large language models (LLMs) offer promising, though underexplored, potential to scaffold these complex learning processes by providing real-time explanations, supporting reaso…

Health

From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms to Foster Dignified Human-AI Interaction

In the future of work discourse, AI is touted as the ultimate productivity amplifier. Yet, beneath the efficiency gains lie subtle erosions of human expertise and agency. This paper shifts focus from the future of work to the future of workers by navigating the AI-as-Amplifier Paradox: AI’s dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise. We present a year-long study on the longitudinal use of AI in a high-stakes workplace among cancer specialists. In…