All traces

Over-reliance on AI tools is associated with diminished analytical skills, reduced critical thinking and engagement, and ethical risks including academic dishonesty and data privacy issues.

Bias in training data and other issues including ethics, replication, environmental impact, and proliferation of low-quality research can negatively impact social science research using generative AI.

Same review identifies persistent risks in those settings including data privacy violations, algorithmic bias, unequal access, and erosion of relational and cultural aspects of teaching, described as an empathy gap in AI tools.

Integrating Google Gemini into education presents significant challenges and ethical considerations that must be addressed.

Generative AI may expand misinformation, distribute workplace benefits unevenly, widen the digital divide in education, and deepen pre-existing inequalities in healthcare.

Current copyright regimes are ineffective at assigning ownership and authorship for music autonomously composed by non-human AI creators.

Use of the AI system for traffic violation appeals impacts decisions made by legal experts and creates tensions between street-level bureaucrats, screen-level bureaucrats and street-level algorithms.

ChatGPT responses contained incorrect information in more than one instance and were written at a college graduate reading level, requiring caution for patient education.

Generative AI may amplify existing dysfunctions of streaming platforms, threaten livelihoods of music professionals, and raise governance and transparency concerns.

Applying conventional Western-rooted AI tools to Mijikenda musical heritage risks perpetuating digital colonialism through decontextualisation of sacred practices, infringement of data sovereignty, and cultural appropriation.

University students reported privacy concerns, technophobia, and guilt feelings that reduced behavioral intention to adopt ChatGPT for learning.

Despite parallels to coding, LLMs still struggle with formalized mathematics, where proof synthesis remains brittle and advances have been significantly more challenging.

Smart sensor deployment in precision farming is limited by calibration issues, data privacy concerns, interoperability gaps and adoption barriers.

AI-powered consumer contracts introduce risks of bias, opacity, privacy violations, and power asymmetries due to limited human intervention.

In creative applications, AI hallucinations require essential human oversight for creative direction, alongside emerging challenges of copyright concerns, bias mitigation, high computational demands, and lack of robust regulatory frameworks.