Athanassopoulos, L. (2026).
Journal of Psychology and AI, 2(1).
Abstract
This study explores the feasibility of integrating artificial intelligence (AI) into counsellor training to enhance feedback quality and scalability. Using Natural Language Processing (NLP), simulated counselling transcripts were analysed across three therapeutic modalities: Person-Centred Therapy (PCT), Pluralistic Therapy, and Cognitive Behavioural Therapy (CBT). NLP, a branch of AI that combines computational linguistics and machine learning, enables systems to interpret and generate human language. The researcher, a qualified psychotherapist and educator, constructed simulated transcripts that were anonymously reviewed by colleagues practising in the respective modalities. The fine-tuned NLP system evaluated key therapeutic markers, including empathy, relational depth, cognitive restructuring, and responsiveness to client preferences. It also demonstrated safeguarding potential by detecting linguistic indicators of suicidal ideation. Findings suggest that AI has the potential to identify modality-specific therapeutic elements and provide consistent, actionable feedback aligned with training benchmarks. However, challenges remain in capturing nonverbal cues and ensuring adaptability across diverse contexts and practitioner styles. Ethical integration within reflective, practitioner-led training frameworks is essential. Overall, AI has the potential to augment human supervision by offering timely, structured, and scalable insights, provided its use is ethically governed and firmly embedded within reflective, practitioner-led training frameworks.
Here are some thoughts:
The study found that the AI showed genuine promise in identifying modality-specific therapeutic markers such as empathy and congruence in PCT, cognitive distortions in CBT, and collaborative decision-making in Pluralistic Therapy, and could provide structured, consistent feedback aligned with professional training standards. Notably, the system also demonstrated potential as a safeguarding tool by detecting linguistic indicators of suicidal ideation and emotional distress, areas where trainee counsellors may lack experience.
However, the study also highlights significant limitations. The AI struggled with nuanced emotional attunement, nonverbal cues, and the subtler relational dimensions of therapy that are central to effective practice. Ethical concerns around algorithmic bias, data privacy, cultural adaptability, and the risk of over-reliance on automated feedback are also raised. The author concludes that AI holds real transformative potential for counsellor education, but must function as a supplement to rather than a replacement for human supervision, and must be embedded within ethically governed, practitioner-led training frameworks.
