<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE trials [
<!ELEMENT trials (trial+)>

<!ELEMENT trial (main,contacts,countries,criteria,health_condition_code,health_condition_keyword,intervention_code,
          intervention_keyword,primary_outcome,secondary_outcome,secondary_sponsor,secondary_ids,source_support,ethics_reviews)>

<!ELEMENT main (trial_id,utrn?,reg_name,date_registration,primary_sponsor,public_title,acronym?,scientific_title,scientific_acronym?,
          date_enrolment,type_enrolment,target_size,recruitment_status,url?,study_type,study_design,phase,hc_freetext?,i_freetext?,results_actual_enrolment,results_date_completed,results_url_link,results_summary,           results_date_posted,results_date_first_publication,results_baseline_char,results_participant_flow,results_adverse_events,results_outcome_measures,results_url_protocol,results_IPD_plan, results_IPD_description)>
<!ELEMENT trial_id (#PCDATA)>
<!ELEMENT utrn (#PCDATA)>
<!ELEMENT reg_name (#PCDATA)>
<!ELEMENT date_registration (#PCDATA)><!-- dd/mm/yyyy -->
<!ELEMENT primary_sponsor (#PCDATA)>
<!ELEMENT public_title (#PCDATA)>
<!ELEMENT acronym (#PCDATA)>
<!ELEMENT scientific_title (#PCDATA)>
<!ELEMENT scientific_acronym (#PCDATA)>
<!ELEMENT date_enrolment (#PCDATA)><!-- dd/mm/yyyy -->
<!ELEMENT type_enrolment (#PCDATA)>
<!ELEMENT target_size (#PCDATA)>
<!ELEMENT recruitment_status (#PCDATA)><!-- Pending,Recruiting,Suspended,Complete,Other -->
<!ELEMENT url (#PCDATA)>
<!ELEMENT study_type (#PCDATA)><!-- interventional,observational -->
<!ELEMENT study_design (#PCDATA)>
<!ELEMENT phase (#PCDATA)>
<!ELEMENT hc_freetext (#PCDATA)>
<!ELEMENT i_freetext (#PCDATA)>
<!ELEMENT results_actual_enrolment (#PCDATA)>
<!ELEMENT results_date_completed (#PCDATA)><!-- dd/mm/yyyy -->
<!ELEMENT results_url_link (#PCDATA)>
<!ELEMENT results_summary (#PCDATA)>
<!ELEMENT results_date_posted (#PCDATA)><!-- dd/mm/yyyy -->
<!ELEMENT results_date_first_publication (#PCDATA)><!-- dd/mm/yyyy -->
<!ELEMENT results_baseline_char (#PCDATA)>
<!ELEMENT results_participant_flow (#PCDATA)>
<!ELEMENT results_adverse_events (#PCDATA)>
<!ELEMENT results_outcome_measures (#PCDATA)>
<!ELEMENT results_url_protocol (#PCDATA)>
<!ELEMENT results_IPD_plan (#PCDATA)>
<!ELEMENT results_IPD_description (#PCDATA)>


<!ELEMENT contacts (contact+)>
<!ELEMENT contact (type,firstname,middlename,lastname,address,city,country1,zip,telephone,email,affiliation)>
<!ELEMENT type (#PCDATA)><!-- Public,Scientific -->
<!ELEMENT firstname (#PCDATA)>
<!ELEMENT middlename (#PCDATA)>
<!ELEMENT lastname (#PCDATA)>
<!ELEMENT address (#PCDATA)>
<!ELEMENT city (#PCDATA)>
<!ELEMENT country1 (#PCDATA)>
<!ELEMENT zip (#PCDATA)>
<!ELEMENT telephone (#PCDATA)>
<!ELEMENT email (#PCDATA)>
<!ELEMENT affiliation (#PCDATA)>

<!ELEMENT countries (country2+)>
<!ELEMENT country2 (#PCDATA)>

<!ELEMENT criteria (inclusion_criteria,agemin,agemax,gender,exclusion_criteria)>
<!ELEMENT inclusion_criteria (#PCDATA)>
<!ELEMENT agemin (#PCDATA)>
<!ELEMENT agemax (#PCDATA)>
<!ELEMENT gender (#PCDATA)>
<!ELEMENT exclusion_criteria (#PCDATA)>

<!ELEMENT health_condition_code (hc_code+)>
<!ELEMENT hc_code (#PCDATA)>

<!ELEMENT health_condition_keyword (hc_keyword+)>
<!ELEMENT hc_keyword (#PCDATA)>

<!ELEMENT intervention_code (i_code+)>
<!ELEMENT i_code (#PCDATA)>

<!ELEMENT intervention_keyword (i_keyword+)>
<!ELEMENT i_keyword (#PCDATA)>

<!ELEMENT primary_outcome (prim_outcome+)>
<!ELEMENT prim_outcome (#PCDATA)>

<!ELEMENT secondary_outcome (sec_outcome+)>
<!ELEMENT sec_outcome (#PCDATA)>

<!ELEMENT secondary_sponsor (sponsor_name+)>
<!ELEMENT sponsor_name (#PCDATA)>

<!ELEMENT secondary_ids (secondary_id+)>
<!ELEMENT secondary_id (sec_id,issuing_authority)>
<!ELEMENT sec_id (#PCDATA)>
<!ELEMENT issuing_authority (#PCDATA)>

<!ELEMENT source_support (source_name+)>
<!ELEMENT source_name (#PCDATA)>

<!ELEMENT ethics_reviews (ethics_review+)>
<!ELEMENT ethics_review (status,approval_date,contact_name,contact_address,contact_phone,contact_email)>
<!ELEMENT status (#PCDATA)><!-- Not approved,Approved,NA -->
<!ELEMENT approval_date (#PCDATA)><!-- dd/mm/yyyy -->
<!ELEMENT contact_name (#PCDATA)>
<!ELEMENT contact_address (#PCDATA)>
<!ELEMENT contact_phone (#PCDATA)>
<!ELEMENT contact_email (#PCDATA)>
]>
<trials>
  <trial>
    <main>
      <trial_id>IRCT20260904070913N1</trial_id>
      <utrn></utrn>
      <reg_name>IRCT</reg_name>
      <date_registration>2026-09-21</date_registration>
      <primary_sponsor>Islamic Azad University</primary_sponsor>
      <public_title>The impact of artificial intelligence on creativity and stress reduction</public_title>
      <acronym></acronym>
      <scientific_title>The impact of artificial intelligence on creativity and stress reduction among students</scientific_title>
      <scientific_acronym></scientific_acronym>
      <date_enrolment>2026-10-01</date_enrolment>
      <type_enrolment>anticipated</type_enrolment>
      <target_size>30</target_size>
      <recruitment_status>Complete</recruitment_status>
      <url>https://irct.ir/trial/93080</url>
      <study_type>interventional</study_type>
      <study_design>Randomization: Randomized, Blinding: Double blinded, Placebo: Not used, Assignment: Parallel, Purpose: Education/Guidance, Randomization description: In this study, a simple randomization method is used to assign participants to two experimental and control groups. The randomization unit in this study is the students of Islamic Azad University, Izeh Branch, who are eligible to enter the study. First, a complete list of volunteers who have been selected based on the study entry criteria is prepared. Then, each participant is assigned a unique numerical identification code. These codes are entered into the Random Number Generator software and a random number is generated for each individual. After generating these numbers, the participants are sorted in ascending order based on the value of the random number. Then, the individuals are assigned to two groups; sequentially, the first 15 people on the list are assigned to the experimental group (training and intervention based on artificial intelligence) and the next 15 people are assigned to the control group. With this method, the order of entry into the study and the researcher's knowledge of the participants will not play any role in determining the group. To conceal allocation, the person responsible for entering data and performing the randomization process in the software will be independent of the intervention administrators and research evaluators (measurers of creativity and stress variables) and will not have the opportunity to know the process of implementing the sessions or changes in the allocation of individuals. Also, the identification codes of the participants will be kept confidential and separate from their other personal information. Therefore, it will not be possible to predict the allocated group for each participant before the randomization process, Blinding description: In order to control bias and improve the internal validity of the research, this study was designed with a double-blind protocol; in this way, the intervention implementers, outcome assessor, and data analyst will be kept unaware of the allocation of students of Islamic Azad University, Izeh Branch to the experimental and control groups. In this process, the outcome assessor will collect data (evaluate creativity and stress variables) in all stages of pre-test, post-test, and follow-up, without knowing the assigned group of the participants, in order to prevent bias in the measurement; also, all raw data will be provided to the statistical analyst with anonymous codes (such as groups 1 and 2), and the true nature of the groups will not be revealed until the end of the data analysis process. All allocation steps will be performed by an independent person from the research team, and decoding of allocation codes will be done only after the final completion of the statistical analyses.</study_design>
      <phase>N/A</phase>
      <hc_freetext>Stress in students.</hc_freetext>
      <i_freetext>Intervention 1: Intervention group: AI-based training sessions will be implemented in the form of a specific training program. The sessions of this intervention will be conducted in 8 90-minute sessions (one session per week) in a group for the experimental group in the statistics course. AI-based training is an approach that aims to reduce anxiety and academic stress, and develop problem-solving skills and creative thinking through interaction with intelligent analytical tools, helping students manage their reactions to the computational and conceptual challenges of statistics courses by replacing traditional and anxiety-provoking learning patterns with interactive and personalized learning. This approach has main educational axes that lead to the promotion of creativity and improvement of statistical skills; These axes include: familiarization with AI tools and language models in data analysis, explanation of how to write precise prompts to understand complex statistical concepts, training in the use of AI to visualize and analyze statistical data, training in idea generation techniques and creative solution of statistical problems with the help of smart tools, management and reduction of stress in solving mathematical and statistical problems through immediate feedback from AI tools, error detection and interpretation of statistical outputs guided by AI, identification of challenges and ethical and critical application of AI in learning, and finally consolidation and summarization of skills learned in statistics lessons. The main emphasis of this intervention is to reduce computational stress and fear of statistics when faced with complex data and replace it with creative, exploratory, and problem-solving approaches based on new technologies. Intervention 2: Control group: They do not receive any intervention or treatment.</i_freetext>
      <results_actual_enrolment></results_actual_enrolment>
      <results_date_completed></results_date_completed>
      <results_url_link></results_url_link>
      <results_summary></results_summary>
      <results_date_posted></results_date_posted>
      <results_date_first_publication></results_date_first_publication>
      <results_baseline_char></results_baseline_char>
      <results_participant_flow></results_participant_flow>
      <results_adverse_events></results_adverse_events>
      <results_outcome_measures></results_outcome_measures>
      <results_url_protocol></results_url_protocol>
      <results_IPD_plan>No - There is not a plan to make this available</results_IPD_plan>
      <results_IPD_description>Justification or reason for not sharing IPD is Participant data is completely confidential and I do not intend to publish it.</results_IPD_description>
    </main>
    <contacts>
      <contact>
        <type>public</type>
        <firstname>Azar Rafiei</firstname>
        <middlename></middlename>
        <lastname></lastname>
        <address>No. 2, Erfan Alley, Kaveh St., Imam Reza Blvd.</address>
        <city>Izeh</city>
        <country1>Iran (Islamic Republic of)</country1>
        <zip>6391937187</zip>
        <telephone>+98 916 811 3102</telephone>
        <email>azar68882@gmail.com</email>
        <affiliation>Asadabad University of Medical Sciences</affiliation>
      </contact>
      <contact>
        <type>scientific</type>
        <firstname>Azar Rafiei</firstname>
        <middlename></middlename>
        <lastname></lastname>
        <address>No. 2, Erfan Alley, Kaveh St., Imam Reza Blvd.</address>
        <city>Izeh</city>
        <country1>Iran (Islamic Republic of)</country1>
        <zip>6391937187</zip>
        <telephone>+98 916 811 3102</telephone>
        <email>azar68882@gmail.com</email>
        <affiliation>Asadabad University of Medical Sciences</affiliation>
      </contact>
    </contacts>
    <countries>
      <country2>Iran (Islamic Republic of)</country2>
    </countries>
    <criteria>
      <inclusion_criteria>Be at least 19 and no more than 45 years old
Be a student at Azad University, Izeh Branch
Be willing to participate in the research</inclusion_criteria>
      <agemin>18 years</agemin>
      <agemax>45 years</agemax>
      <gender>Both</gender>
      <exclusion_criteria>Have a physical illness that interferes with the treatment process
Be taking psychiatric medications</exclusion_criteria>
    </criteria>
    <health_condition_code>
      <hc_code>Z73.3</hc_code>
    </health_condition_code>
    <health_condition_keyword>
      <hc_keyword>Stress, not elsewhere classified</hc_keyword>
    </health_condition_keyword>
    <intervention_code>
      <i_code>Behavior</i_code>
      <i_code>N/A</i_code>
    </intervention_code>
    <intervention_keyword>
      <i_keyword>Intervention group: AI-based training sessions will be implemented in the form of a specific training program. The sessions of this intervention will be conducted in 8 90-minute sessions (one session per week) in a group for the experimental group in the statistics course. AI-based training is an approach that aims to reduce anxiety and academic stress, and develop problem-solving skills and creative thinking through interaction with intelligent analytical tools, helping students manage their reactions to the computational and conceptual challenges of statistics courses by replacing traditional and anxiety-provoking learning patterns with interactive and personalized learning. This approach has main educational axes that lead to the promotion of creativity and improvement of statistical skills; These axes include: familiarization with AI tools and language models in data analysis, explanation of how to write precise prompts to understand complex statistical concepts, training in the use of AI to visualize and analyze statistical data, training in idea generation techniques and creative solution of statistical problems with the help of smart tools, management and reduction of stress in solving mathematical and statistical problems through immediate feedback from AI tools, error detection and interpretation of statistical outputs guided by AI, identification of challenges and ethical and critical application of AI in learning, and finally consolidation and summarization of skills learned in statistics lessons. The main emphasis of this intervention is to reduce computational stress and fear of statistics when faced with complex data and replace it with creative, exploratory, and problem-solving approaches based on new technologies.</i_keyword>
      <i_keyword>Control group: They do not receive any intervention or treatment.</i_keyword>
    </intervention_keyword>
    <primary_outcome>
      <prim_outcome>Creativity. Timepoint: before performing the intervention and two weeks after completion of the intervention. Method of measurement: Torrance Creativity Questionnaire 1979.</prim_outcome>
      <prim_outcome>Stress. Timepoint: before performing the intervention and two weeks after completion of the intervention. Method of measurement: Fathi et al. Stress Questionnaire 2004.</prim_outcome>
    </primary_outcome>
    <secondary_outcome>
      <sec_outcome></sec_outcome>
    </secondary_outcome>
    <secondary_sponsor>
      <sponsor_name></sponsor_name>
    </secondary_sponsor>
    <secondary_ids>
      <secondary_id>
        <sec_id></sec_id>
        <issuing_authority></issuing_authority>
      </secondary_id>
    </secondary_ids>
    <source_support>
      <source_name>Islamic Azad University</source_name>
    </source_support>
    <ethics_reviews>
      <ethics_review>
        <status>Approved</status>
        <approval_date>2026-07-28</approval_date>
        <contact_name>Behbahan Islamic Azad University Ethics Committee</contact_name>
        <contact_address>Shahid Bayani St., Amir Kabir Blvd. بهبهان Khouzestan Iran (Islamic Republic of)</contact_address>
        <contact_phone></contact_phone>
        <contact_email></contact_email>
      </ethics_review>
    </ethics_reviews>
  </trial>
</trials>
