Sep 2026· The Counseling Psychologist· 15 references
Artificial Intelligence in Healthcare and EducationDigital Mental Health Interventions
Abstract
As generative artificial intelligence (AI) tools like ChatGPT become increasingly accessible, counseling psychologists are exploring their potential use in clinical practice, often without clear guidance or ethical frameworks. This paper presents a counselor-led model for integrating generative AI tools into counseling practice. Grounded in ethical principles, the model emphasizes human oversight, transparency, and client-centered care. A case study illustrates the application of this model with a veteran receiving counseling services through the Veterans Health Administration. The case demonstrates how generative AI can support foundational career counseling tasks (i.e., career exploration and career planning), while maintaining full counselor oversight. Key ethical considerations are discussed, including transparency and informed consent, data privacy and security, accuracy and bias, and human oversight and professional judgment. This work contributes to the emerging literature on AI-assisted counseling by offering an ethically grounded and practice-oriented model for counseling professionals interested in engaging with these technologies responsibly.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
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