Does a digital human improve AI stress counselling?

Seventy-one students completed the randomised comparison

The team recruited 97 students aged 18 to 25 from Beijing Normal University. Following a pre-test, 71 who met predefined criteria were included: they had mild-to-moderate anxiety or depression and scored above five on willingness to seek psychological support. Thirty-six were assigned to DHCBT and 35 to the text-only condition. The gender counts were closely balanced within both groups, although the paper does not establish representativeness beyond this single university.

Participants gave written informed consent and the Beijing Normal University ethics committee approved the study under protocol 202406210122. After their assigned single session, students completed scales covering cognitive load, social presence, system usability and intention to keep using the technology, then joined a short interview. The PHQ-9 and GAD-7 were part of screening, but the results section does not report a pre-to-post symptom comparison. That omission prevents claims that either system reduced depression, anxiety or academic stress.[1]

The avatar improved experience scores, not a clinical endpoint

On seven-point cognitive-load scales, the digital-human group reported lower intrinsic load, with a mean of 3.67 versus 4.76 for text, and lower extraneous load, 3.04 versus 4.34. The reported standardised differences were large and the two-sided tests had p values below 0.001. System Usability Scale scores averaged 73.48 for DHCBT and 62.78 for text; technology-use intention averaged 4.67 versus 3.23, again with reported p values below 0.001.

Social-presence scores were higher with the avatar, 7.12 versus 6.82, but the reported p value was 0.062. Under the usual 0.05 convention, that comparison is not statistically significant. The paper’s abstract and discussion sometimes group social presence with the system’s advantages, so readers should keep the actual result in view. Self-reported intent to reuse is also not observed continued use, and usability is not evidence that advice is accurate or improves mental health.

Interview responses were generally positive: 80.5% were described as favourable overall, 86.1% reported substantial help and 52.8% mentioned response speed as an area to improve. These percentages are perceptions gathered immediately after the encounter. The publication does not describe blinded qualitative coding, a preregistered interview analysis or follow-up to determine whether positive impressions persisted.[1]

Benchmark results broaden the evaluation but bring new biases

Separate from the user experiment, the multi-agent backend was tested on three CBT-BENCH classification sets containing 146, 184 and 112 cases. It was also compared with DeepSeekV3-1, GLM-4.5 and CBT-LLM on 60 academic-stress prompts drawn from 120 de-identified posts collected on two public Chinese counselling platforms. Nine psychology-counselling students—three undergraduates, three master’s students and three doctoral students—rated anonymised responses for safety, CBT process, problem comprehension and empathy.

The proposed system had the highest aggregated human ratings across all four dimensions and overall inter-rater reliability of 0.750. Reliability for safety alone was only 0.475, showing substantial disagreement on the dimension with the greatest potential consequences. Automatic ratings used GPT-5 and Claude Opus 4.1, creating a risk that language models reward styles similar to their own. Neither benchmark demonstrated safe behaviour in unscripted, prolonged or acute real-world use.[1]

The safety boundary is narrower than many crises

DHCBT screens inputs for offensive or illegal material and explicit expressions of suicide or self-harm. When it detects explicit high risk, the routine flow stops and the user is directed to a crisis hotline or immediate professional support. Generated replies also undergo a compliance review and can be regenerated. Those are meaningful design controls, but the authors state that the current mechanism does not reliably identify subtler crisis signals such as hopelessness without direct self-harm language.

That limitation is especially important because the enrolled students had mild-to-moderate symptoms, not a verified absence of evolving risk. An engaging human-like avatar may increase disclosure and trust, but it may also make a system’s limitations less visible. Any deployment would need validated risk escalation, local emergency information, clinician oversight where appropriate, clear disclosure that the system is not a person, privacy controls for sensitive speech and transcripts, and monitoring for harmful or overconfident advice.[1]

What would establish benefit rather than appeal

The next trial should be preregistered, larger and multi-site, with allocation concealment, attrition reporting and outcome assessors insulated from condition where possible. It should measure academic stress, anxiety, depression, functioning, adverse events and help-seeking over weeks or months—not only experience immediately after one session. Comparators should include ordinary support or clinician-guided digital CBT where ethically appropriate, while factorial designs could separate voice, facial animation, embodiment and multi-agent workflow.

The authors are affiliated with Beijing University of Posts and Telecommunications and China Mobile Group Device. Chinese national research funds supported the work, and the authors declared no competing interests. The dataset is available only from the corresponding author on reasonable request. The study supports a preliminary claim that one avatar interface felt easier and more usable than the same text backend for selected students. It does not yet show that a digital human delivers effective therapy or safely substitutes for qualified care.[1]