The main aim of the present study is to investigate the effects of a Motivational Interviewing-based artificial intelligence chatbot on affective arousal in college students with elevated levels of depression and anhedonia by combining a randomized active-control intervention design with pre- and post-intervention emotional video task assessments during fMRI.
Anhedonia represents a core characteristic of depression and is characterized by reduced experience of pleasure. It is closely related to decreased motivation, altered reward processing, changes in affective responsiveness, and alterations in intrinsic brain network function. Anhedonia is not specifically targeted by currently available pharmacological interventions. Initial evidence indicates that an increased willingness to change and implementation of change in daily life can alleviate anhedonia. The present study aims to examine whether a Motivational Interviewing-based AI chatbot can lead to changes in affective arousal responses in college students with elevated anhedonia and depressive symptoms. Affective arousal is included because anhedonia may involve altered emotional reactivity and reduced subjective responses to affective stimuli, in addition to reduced pleasure. The emotional video task allows the study to assess subjective arousal responses and neural responses to positive, neutral, and negative affective stimuli. To this end, eligible participants with a total score of 22 or higher on the Snaith-Hamilton Pleasure Scale and a score of 14 or higher on the Beck Depression Inventory will undergo a randomized, between-subjects, active-control intervention study. Participants will be assigned to either a Motivational Interviewing-based chatbot group or an active control chatbot group for 1 week. Pre- and post-intervention assessments will include self-report questionnaires and an affective arousal video task during functional magnetic resonance imaging to examine psychological, behavioral, and neural effects of the intervention.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
BASIC_SCIENCE
Masking
DOUBLE
Enrollment
80
The experimental chatbot is designed to use principles of Motivational Interviewing to support participants in exploring their personal values, motivation for change, and daily behavioral goals related to pleasure, engagement, and reward-seeking. During the intervention period, participants will interact with the chatbot regularly through brief text-based conversations. The chatbot will provide empathic, non-judgmental responses, encourage reflection on current difficulties, and help participants identify small, feasible actions that may increase daily engagement and positive experiences. It will not provide diagnosis, crisis counseling, or medical treatment.
Participants will interact with a chatbot matched in format and frequency of use. This chatbot will provide neutral nature-related stories or general natural history content. It will be designed to maintain participant engagement while avoiding therapeutic techniques, motivational interviewing strategies, behavioral activation guidance, or personalized mental health advice. This active control condition will help control for nonspecific effects of chatbot interaction, attention, expectancy, and digital engagement.
University of Electronic Science and Technology of China
Chengdu, Sichuan, China
RECRUITINGSubjective Affective Arousal in Response to Video Stimuli
Participants will view positive, neutral, and negative video stimuli before and after the intervention and report their subjective arousal after each video. Mean arousal ratings will be calculated separately for each emotional condition, and pre-to-post changes will be compared across positive, neutral, and negative video conditions
Time frame: Baseline before the first chatbot interaction and Week 1 after completion of the chatbot intervention.
Activity in Brain Systems Supporting Affective Arousal Processing
Participants will undergo task-based BOLD fMRI while viewing positive, negative, and neutral emotional videos. Neural responses within predefined brain systems involved in arousal and valence processing will be estimated using a general linear model. Beta contrast estimates will then be derived for positive versus neutral, negative versus neutral, and positive versus negative conditions. These estimates will be used to evaluate changes from baseline to post-intervention.
Time frame: Baseline before the first chatbot interaction and Week 1 after completion of the chatbot intervention.
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