Exploring ChatGPT's Capabilities, Stability, Potential and Risks in Conducting Psychological Counseling through Simulations in School Counseling
November 03, 2025 Β· Declared Dead Β· π Mental Health and Digital Technologies
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yang Ni, Yanzhuo Cao
arXiv ID
2511.01788
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY
Citations
2
Venue
Mental Health and Digital Technologies
Last Checked
4 months ago
Abstract
This study explores ChatGPT's capabilities, stability, and risks in simulating psychological counseling sessions in a school counseling context. Using scripted role-plays between a human counselor and an AI client, we examine how a large language model performs core counseling skills such as empathy, reflection, summarizing, and asking open-ended questions, as well as its ability to maintain therapeutic communication over time. We focus on how consistently ChatGPT can behave like a "virtual client" for school counselors in training, and how its responses might support or disrupt counselor skill development, supervision, and practice. At the same time, we analyze potential risks, including inaccurate or unsafe suggestions, over-compliance with counselor prompts, and the illusion of a competent therapist where no real professional judgment exists. The findings suggest that ChatGPT can serve as a low-cost, always-available training tool for practicing counseling techniques and interviewing skills in education and mental health settings, but it should not be viewed as a replacement for a human therapist or school counselor. We propose practical guidelines for educators, supervisors, and researchers who wish to use ChatGPT or similar LLM-based conversational agents in counseling training, highlighting how to leverage its potential while managing ethical, pedagogical, and psychological risks.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted