Applied Social Studies - Conference Items

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    Technology enabled collaboration in Irish social work practice education, CPD and research: presenting themes from the national practice teaching in social work initiative
    (University College Cork, 2024) Rose, Joanne; Murphy, Niamh; O'Connor, Erna; Byrne-Cummins, Jean; Dorney, Lyn; Slavin, Paula; Kelly, Eleanor; Murphy, Clíona; Department of Children, Equality, Disability, Integration, and Youth, Ireland
    Challenges posed by existing & contemporary crises in social work student placements led to the opportunity for a new and innovative collaboration of Irish practice education coordinators from six higher education institutions with IASW Outputs include: • Learning & teaching resource website • Open access and free to attend online CPD • Practice teaching and education research
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    Unlocking the impact of inclusive, student-led participatory research on placement: a guide to lead and succeed in a digitally enhanced learning environment in higher education
    (Centre for the Integration of Research, Teaching and Learning, University College Cork, 2023) McLaughlin, Amy; Stoyle, Kate; Rose, Joanne; Russ, Erica; Short, Monica; Halton, Carmel
    Since 2019 social work fieldwork researchers from UCC and Australia have been discussing challenges within field education placements both the general scarcity of placements and the impact of COVID-19. This led to an invitation for UCC to participate from 2021-2023 in two transnational student-led research projects. The placement environment is a supportive one that embodies principles of trust-building and learning by doing. In this project, a safe collaborative online space was created for students and academics to work together, share ideas and knowledge, and learn collaboratively about the research process. Students on placement in both Ireland and Australia worked together to co-write a journal article on a mutually agreed topic.
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    Participatory research upholding collaboration in research and practice in social work fieldwork education
    (European Congress of Qualitative Inquiry, 2024-10-01) Rose, Joanne; Short, Monica; Russ, Erica; Petrakis, Melissa; Halton, Carmel
    Internationally minimum standards for social work education specified by the International Federation of Social Workers include the ability to demonstrate a critical understanding of social research and the principles, ethics, and applications of scientific inquiry. Co-operative inquiry is a collaborative research methodology suited to field education research as, consistent with social work principles, it is respectful and participatory, and upholds the unique dignity of each person involved in the research. An inquiry process is grounded in the idea that research can be conducted with people, not on people. Co-operative inquiry highlights the principle of the equality of voices within the co-authoring process; this is made possible because students and academics have power over what is written and published. It brings students and academics together as co-authors, co-participants, and co-researchers. A community of trust is developed, combining participants’ values with their professional or personal knowledge and experience. Considering the tensions and pressures inherent in neoliberal higher education, this paper will remind us of the codes of ethics of the Australian Association of Social Work, the Social Work Registration Board (CORU), and the Irish Association of Social Workers and emphasise the importance of collaboration and respect concerning social justice, and social theory research. The outcomes of three student inquiries will be shared, including how they led to students connecting to an international research network, presenting at international conferences, and publishing their work in international academic journals. Student participant feedback indicates the value of their involvement in co-operative inquiry as traditional research typically does not include student voices, which is why this research was special because it was led by students and gave them more significance.
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    Teaching practices and reflections on AIGC in brand advertising design
    (Springer Nature, 2024-06-08) Wei, Dong; Li, Lingxuan; You, Zongyuan
    Generative Artificial Intelligence (AIGC) technology has been quickly evolving since 2022, with advertising becoming one of its most popular applications. This opens up new potential and difficulties for current and future brand advertising design education. This article investigates AIGC's design thinking transformation in brand advertising design using the course “AIGC: Machine-Assisted Innovative Design for Brand Advertising” offered at Communication University of China (CUC) during the summer semester of 2023. The goal is to help students comprehend AI art and machine learning, to break down the homogenization of AIGC-generated outcomes from an art and design standpoint, and to develop students’ critical thinking skills so that they can reconsider the creative value of the human brain. The course is structured into two parts: the first part focuses on learning and experiencing AIGC technologies such as ChatGPT, Midjourney, Runway, and others, and applying them thoroughly in design practice. The second half of the course focuses on China Chic brand advertisements and uses hand-drawing, computer-aided design, and AICG to create a whole case design. The course findings demonstrate that AIGC tools have a high level of innovation in the creative process, but there are some issues such as uncontrollability and homogeneity that require humans to spend more time and energy adapting. Students agreed that generative AI can be an effective technique to inspire human creativity, and that when employing AIGC tools, designers should look for ways to collaborate with AI rather than viewing it as a total replacement for humans.
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    Privacy preserving loneliness detection: A federated learning approach
    (Institute of Electrical and Electronics Engineers (IEEE), 2022-08-24) Qirtas, Malik Muhammad; Pesch, Dirk; Zafeiridi, Evi; Bantry-White, Eleanor; Science Foundation Ireland
    Today's smartphones have sensors that enable monitoring and collecting data on users' daily activities, which may be converted into behavioral indicators of users' health and well-being. Although previous research has used passively sensed data through smartphones to identify users' mental health state, including loneliness, anxiety, depression, and even schizophrenia, the issue of user data privacy in this context has not been well addressed. Here we focus on the feeling of loneliness, which, if persistent, is associated with a number of negative health outcomes. While modern artificial intelligence technology, specifically machine learning, can assist in detecting loneliness or depression, current approaches have applied machine learning to centrally collected user data at a single location with the potential to compromise user data privacy. To address the issue of privacy, we investigated the feasibility of using federated learning on single user data to identify loneliness collected by different smartphone sensors. Federated learning can help protect user privacy by avoiding the transmission of sensitive data from mobile devices to a central server location. To evaluate the federated method's performance in detecting loneliness, we also trained models on all user data using a centralised machine learning approach and compared the results. The results indicate that federated learning has considerable promise for detecting loneliness in a binary classification problem while maintaining user data privacy.