{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:41:12Z","timestamp":1783611672653,"version":"3.55.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Effective mental health education requires personalized recommendations and intervention strategies that align with an individual's psychological condition and learning preferences. This study introduces a Reinforcement Learning-Based Adaptive Mental Health Advisor (RL-AMHA) that selects instructional content and self-care interventions using a PPO agent and actor-critic neural architecture. The agent uses self-reports, in-app behavior, and contextual cues from a mobile mental health platform. It was trained on 1,200 anonymized user sessions using log data and validated questionnaire scores, after ethics approval and informed consent. The experiment contrasts RL-AMHA with a static rule-based curriculum and a supervised recommendation model that predicts content based on past clicks. All models share the same content pool and interaction constraints; PPO hyperparameters (learning rate, discount factor, clipping range, batch size) were tweaked on a validation split, and 500 episodes were trained until the average episodic reward converged. RL-AMHA improves engagement rate from 75.2% to 88.1% (+2.9%), user satisfaction from 4.21 to 4.85 (+0.64 on a 5-point scale), and weekly self-care activity frequency from 9.4 to 13.1 (+39%) compared to the baseline. Additionally, it enhances stress-reduction scores by 18.6% and increases continuous engagement time from 21.3 to 29.0 minutes. With minimal implementation costs, RL-AMHA demonstrates scalability, adaptability, and effectiveness for long-term psychological support across mobile health, e-learning, and clinical decision-support environments.<\/jats:p>","DOI":"10.31449\/inf.v50i8.11347","type":"journal-article","created":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T11:58:11Z","timestamp":1771761491000},"source":"Crossref","is-referenced-by-count":1,"title":["RL-AMHA: A Reinforcement Learning-Based Adaptive System for Personalized Mental Health Education and Intervention"],"prefix":"10.31449","volume":"50","author":[{"given":"Jine","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11347\/6523","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11347\/6523","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:01:30Z","timestamp":1771840890000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/11347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i8.11347","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}