500 words
Reflective Account – Data Collection Practices
This individual assessment requires students to complete practical data collection exercises and write a 400–500 word reflective account based on their experience. The assessment is designed to develop understanding of different data collection methods and encourage students to connect the practical exercises completed in the laboratory with research methods and concepts covered during lectures. Students are required to complete two main types of data collection practice. The first involves questionnaire tests, which students attend during their allocated practical sessions. The second involves a series of recorded interaction exercises involving ChatGPT and human participants. Students must follow the supplied study instructions, use the specified prompts where required, record the requested activities using a camera such as a phone, and upload the relevant files to a designated OneDrive folder. The briefing also requires students to complete and sign the participant consent form and submit it with their collected data. The reflective account should summarise the methods learned during the module lectures and the methods exercised during the laboratory sessions. Students should demonstrate their overall understanding of how data collection works, why data collection is important in research, and how knowledge of data collection methods can be improved through practical experience. The reflection must address several key research and methodological questions. Students should explain how they would design a research question based on the collected data and identify the PICO elements relevant to the research. They should discuss what type of research could be conducted using the data and define an example of a machine learning problem that could be investigated. The reflection should also consider the participant experience by identifying areas of the data collection process that could be improved. Students are additionally asked to consider how they would design a similar data collection project themselves and whether they would make any changes to the methods, procedures or participant experience. The assessment therefore combines practical experience with critical reflection on research design, data collection, research questions, PICO, machine learning applications and methodological improvement. The final reflective account is a Canvas submission and should remain within the specified 400–500 word range. The assessment briefing states that students are marked based on the submitted report, making the reflective account the primary assessed component of the activity.
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Identifying Psychological States of Human Subjects via Machine Learning Algorithms
This research project investigates the application of artificial intelligence and machine learning techniques to the identification of human psychological states. The study collects multimodal information from participants during structured conversational interactions and combines these data with established psychological and demographic measures. The research explores whether machine learning algorithms can identify patterns associated with psychological states using behavioural, physiological and conversational information. Participants take part in structured conversations involving both artificial intelligence and another human participant. The conversational activities include emotional reflection, cognitive challenges and open-ended discussion. During the emotional reflection activity, participants discuss experiences such as happiness, stress and coping strategies. The cognitive component includes memory tasks, riddles, planning questions and problem-solving or moral-dilemma questions. The final component involves an open conversation around topics such as travel, artificial intelligence, books or films. The ChatGPT interaction is designed to provide a consistent conversational structure while allowing participants to respond naturally. The study collects several forms of multimodal data. These include voice recordings, video recordings showing participants' facial expressions, spoken conversation transcripts and EEG data. Additional demographic and lifestyle information may also be collected. The research materials describe factors such as age, marital status, weight, height, disability, smoking, drinking, academic performance, family income, family size and physical exercise. Established questionnaires are also used to obtain information about participants' experiences and psychological states. Machine learning techniques are subsequently applied to the collected datasets to identify patterns that may be associated with psychological states. The research materials describe potential applications of AI in psychological assessment, predictive analytics, emotion recognition, monitoring and prevention, personalised treatment and AI-assisted mental health support. The broader objective is to investigate whether computational approaches can contribute to earlier identification of psychological difficulties and support more accessible and personalised approaches to mental health assessment. The project also places emphasis on participant welfare, informed consent, confidentiality and secure handling of research data. Participants are informed that they can withdraw their data up to completion of the study and are provided with support information if participation raises concerns or emotional discomfort. The consent documentation confirms that participants are informed about voice, video and photographic recording, data handling and access arrangements. The supplied materials also contain separate instructions for a conversational AI study in which participants interact with ChatGPT for at least ten minutes. The interaction is recorded and the resulting conversation transcript is retained for research purposes. The project therefore provides a multimodal research setting for examining human psychological states through conversational, behavioural and machine-learning approaches.
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