Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction
2026-06-18T07:23:50Z•1c2486d3d03e350d11b89223a9093881902f2a6630cf942c20afb7b3fae53f16
AI ethicsChinaLLMsTikTokalgorithmic biascensorship circumventioncredit scoringdataset releaseeducationhealth misinformationlearning analyticsmisinformationmodel evaluationnetwork measurementpayment systems (Alipay)proxy servicesreject inferencereproductive healthselection biastrust
What happened
Collection of recent arXiv submissions (AI/education/ethics, HCI, social computing, and security) highlighting: (1) signals for adaptive AI-ethics instruction based on LLM usage; (2) AI-driven evaluation linking tutor training to real-life performance (Gemini-2.5-pro, released datasets/prompts); (3) visualization tools and qualitative analytics for learning research; (4) critical humanities analysis of latent diffusion image models; (5) client suspicion of AI in crisis counseling and implications for trust; (6) RELIANCE — an expert-annotated TikTok reproductive-health dataset used to evaluateL
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cy
- Record identifier
- 1c2486d3d03e350d11b89223a9093881902f2a6630cf942c20afb7b3fae53f16
- Enrichment time
- 2026-06-18T07:23:50Z
- AI-assisted enrichment
- Yes
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