Recently Forbes published an article by Lance Eliot announcing that OpenAI plans to augment ChatGPT with an online network of human therapists. The concept is that, when ChatGPT detects signs of distress, it would hand off to a vetted therapist within its platform. This merges AI’s pattern‑recognition abilities with human professional judgment, creating a hybrid model of support.

As someone deeply interested in how schools can responsibly harness AI—especially for qualitative, emotionally nuanced domains like SEMH (Social, Emotional, Mental Health) and SEND (Special Educational Needs and Disabilities)—I see powerful parallels between OpenAI’s approach and the idea of building a private, local LLM inside a school environment that processes daily staff observations to assist in understanding pupil behaviour, regulation, and wellbeing.

1. What the Forbes Article Reveals

The Forbes article outlines OpenAI’s plan to connect ChatGPT with licensed therapists, creating a human‑in‑the‑loop network that ensures users receive appropriate help when showing signs of mental distress. The model blends the scalability of AI with the empathy and accountability of human professionals.

Key Challenges and Considerations

• Scalability: How to serve millions of users with real-time human response capacity.

• Vetting & consistency: Ensuring therapists meet professional standards globally.

• Thresholds: Determining when to route a case to a human.

• Trust & transparency: Users need to understand how the AI decides to escalate.

• Regulatory complexity: Navigating cross-jurisdictional laws and standards of care.

2. Lessons for Education and Local LLMs

The approach offers valuable lessons for how LLMs could support schools in analysing qualitative SEMH and SEND data responsibly. A similar model—AI as observer and amplifier, not decider—can be applied to pupil wellbeing systems.

Key Lessons Mapped to Education

Lesson from OpenAI	Application in Schools (Local LLMs for SEMH / SEND)
Human-in-the-loop	Any AI insight must be reviewed or overridden by a SENCo or teacher.
Clear escalation thresholds	Define rules for low, medium, and high concern triggers.
Explainability	Show why the AI flagged a pattern, using data trends and examples.
Data privacy & ethics	Strong governance, anonymisation, and access control required.
Prioritisation	Avoid alert fatigue by ranking and batching AI suggestions.
Feedback loops	Use staff responses to continuously improve model accuracy.
Safe failure defaults	Uncertain cases should default to human review, not automation.
Trust & legitimacy	Build transparency and involve staff in co‑design to ensure adoption.

3. Architecture for a School LLM

A local LLM could be designed to collect and analyse structured observations, generate insight reports, and flag potential SEMH concerns. Below is a high-level architecture inspired by OpenAI’s human‑in‑the‑loop approach:

• Daily reflections logged by staff capturing strengths, challenges, and context.

• Preprocessing transforms observations into structured, analysable data.

• The local LLM detects patterns and suggests attention levels (monitor, review, refer).

• Human reviewers (SENCo or pastoral team) confirm, modify, or reject AI suggestions.

• Feedback informs continuous retraining and model calibration.

4. Risks and Mitigations

• Alert fatigue: Limit daily flags; prioritise by severity.

• Automation bias: Train staff to treat AI as assistant, not authority.

• False negatives: Review missed cases and recalibrate thresholds.

• Bias: Audit model outputs for subgroup fairness.

• Data drift: Re-evaluate models as pupil cohorts change.

• Privacy: Encrypt data, log access, and maintain compliance.

5. The Takeaway

The big message from OpenAI’s therapist network—and from the broader field of ethical AI—is that technology’s greatest strength lies in partnership with humans. In education, that means building AI tools that listen, notice, and support, while leaving the judgment, empathy, and care firmly with professionals.

By adopting these principles, schools can use local LLMs not to replace pastoral care, but to augment it—making qualitative SEMH analysis more systematic, and giving staff more time to act with insight and compassion.

Read the original Forbes article by Lance Eliot here: https://www.forbes.com/sites/lanceeliot/2025/09/21/openai-aims-to-augment-chatgpt-with-an-online-network-of-human-therapists-which-will-skyrocket-the-need-for-vast-numbers-of-mental-health-professionals/