Application of Artificial Intelligence in Psychological Assessment: Current Status and Challenges

Psychological assessment is an important part of mental health services. Scientific and efficient mental health assessment and intervention are the premise of effective mental health services. Traditional psychological assessment mainly includes psychological tests and clinical interviews. With the rapid development of artificial intelligence and big data technology, psychological assessment is gradually moving towards intelligence. This paper reviews the current application of key technologies of artificial intelligence in psychological assessment, discusses its challenges in psychometric validity, ethics, and technical limitations. The results show that although artificial intelligence has great potential to improve the accessibility, efficiency, and accuracy of psychological assessment, its integration with clinical practice requires careful attention to measurement equivalence, data privacy, algorithmic fairness, and the irreplaceable role of human consultants. Therefore, in the era of artificial intelligence, psychological assessment should continue to develop in the future direction of human-machine collaboration.

References

Ren P, Wang Y, Liu DY, et al., 2022, Intelligent Application of Mental Health Assessment and Intervention. Journal of Beijing Normal University (Social Science Edition), 2022(4): 150–160.

Yao F, Wang X, Wei ZD, et al., 2024, Research Progress of Artificial Intelligence in Psychological Assessment. Science and Technology Review, 42(23): 70–78.

Li WT, 2025, Opportunities and Challenges of Artificial Intelligence in Psychological Counseling and Treatment Services. Psychology Newsletter, 8(2): 93–98.

Cheng MB, Hu XT, Zhou XQ, et al., 2025, Development and Prospect of Intelligent Psychological Assessment. Chinese Journal of Clinical Psychology, 2025(6): 1206–1211. https://doi.org/ 10.16128/j.cnki.1005-3611.2025.06.014

Zhang P, 2020, The Application of AI Technology in College Students’ Psychological Assessment. China Education Informatization, 2020(10): 90–93.

Luxton DD, 2016, Chapter 1 — An Introduction to Artificial Intelligence in Behavioral and Mental Health Care, in Artificial Intelligence in Behavioral and Mental Health Care. Academic Press, San Diego, 1–26. https://doi.org/10.1016/B978-0-12-420248-1.00001-5

Corcoran CM, Carrillo F, Fernández-Slezak D, et al., Prediction of Psychosis across Protocols and Risk Cohorts Using Automated Language Analysis. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 17(1): 67–75. https://doi.org/10.1002/wps.20491

Jaiswal S, Valstar MF, Gillott A, et al., 2017, Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). 2017: 762–769. https://doi.org/10.1109/FG.2017.95

Kalmady SV, Greiner R, Agrawal R, et al., 2019, Towards Artificial Intelligence in Mental Health by Improving Schizophrenia Prediction with Multiple Brain Parcellation ensemble-learning. NPJ Schizophrenia, 5(1): 2. https://doi.org/10.1038/s41537-018-0070-8

Ophir Y, Tikochinski R, Asterhan CSC, et al., 2020, Deep Neural Networks Detect Suicide Risk from Textual Facebook Posts. Scientific Reports, 10(1): 16685. https://doi.org/10.1038/s41598-020-73917-0

Tian X, Batterham P, Song S, et al., 2018, Characterizing Depression Issues on Sina Weibo. International Journal of Environmental Research and Public Health, 15(4): 764. https://doi.org/10.3390/ijerph15040764

Lee J, Robert W, 2018, Neuromarkers for Mental Disorders: Harnessing Population Neuroscience. Frontiers in Psychiatry, 2018(9): 242. https://doi.org/10.3389/fpsyt.2018.00242

Luijten M, Schellekens AF, Kühn S, et al., 2017, Disruption of Reward Processing in Addiction: An Image-Based Meta-analysis of Functional Magnetic Resonance Imaging Studies. JAMA Psychiatry, 74(4): 387–398. https://doi.org/10.1001/jamapsychiatry.2016.3084

Crossley NA, Mechelli A, Ginestet C, et al., 2016, Altered Hub Functioning and Compensatory Activations in the Connectome: A Meta-Analysis of Functional Neuroimaging Studies in Schizophrenia. Schizophrenia Bulletin, 42(2): 434–442. https://doi.org/10.1093/schbul/sbv146

Brühl AB, Delsignore A, Komossa K, et al., 2014, Neuroimaging in Social Anxiety Disorder—A Meta-analytic Review Resulting in a New Neurofunctional Model. Neuroscience and Biobehavioral Reviews, 2014(47): 260–280. https://doi.org/10.1016/j.neubiorev.2014.08.003

Plichta MM, Scheres A, 2014, Ventral–striatal Responsiveness during Reward Anticipation in ADHD and its Relation to Trait Impulsivity in the Healthy Population: A Meta-Analytic Review of the fMRI Literature. Neuroscience & Biobehavioral Reviews, 2014(38): 125–134. https://doi.org/10.1016/j.neubiorev.2013.07.012

Wang YY, Zhan JE, Chen ZL, et al., 2021, Application of Machine Learning in Psychometrics. Computer Knowledge and Technology, 17(3): 204–206. https://doi.org/ 10.14004/j.cnki.ckt.2021.0183

Williamson JR, Young D, Nierenberg AA, et al., Tracking Depression Severity from Audio and Video Based on Speech Articulatory Coordination. Computer Speech & Language, 2019(55): 40–56. https://doi.org/10.1016/j.csl.2018.08.004

Zhao N, Zhang Z, Wang Y, et al., 2019, See your Mental State from Your Walk: Recognizing Anxiety and Depression through Kinect-recorded Gait Data. PLOS ONE, 14(5): e0216591. https://doi.org/10.1371/journal.pone.0216591

Schultebraucks K, Yadav V, Shalev A Y, et al., 2022, Deep Learning-based Classification of Posttraumatic Stress Disorder and Depression Following Trauma Utilizing Visual and Auditory Markers of Arousal and Mood. Psychological Medicine, 52(5): 957–967. https://doi.org/10.1017/S0033291720002718

Liu T, Hou Q, Huang YX, 2022, Research on Mental Health Assessment of College Students Based on Big Data Driven. Information Technology, 2022(10): 41–45. https://doi.org/ 10.13274/j.cnki.hdzj.2022.10.007

Ovsyannikova D, De Mello VO, Inzlicht M, 2025, Third-party Evaluators Perceive AI as more Compassionate than Expert Humans. Communications Psychology, 3(1): 4–14. https://doi.org/10.1038/s44271-024-00182-6

Matan RB, Hadar A, Huppert J, et al., 2024, Considering the Role of Human Empathy in AI-driven Therapy. MIR Mental Health, 2024(11): e56529. https://doi.org/10.2196/56529.

Iwaya LH, Babar MA, Rashid A, et al., 2023, On the Privacy of Mental Health Apps: An Empirical Investigation and its Implications for App Development. Empirical Software Engineering, 28(1): 2–43. https://doi.org/10.1007/s10664-022-10236-0

Sharma A, Lin IW, Miner AS, et al., 2023, Human–AI Collaboration Enables more Empathic Conversations in Text-based Peer-to-peer Mental Health Support. Nature Machine Intelligence, 5(1): 46–57. https://doi.org/10.1038/s42256-022-00593-2

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