Background: App-based mobile health exercise interventions can motivate individuals to engage in more physical activity (PA). According to the Fogg Behavior Model, it is important that the individual receive prompts at the right time to be successfully persuaded into PA. These are referred to as just-in-time (JIT) interventions. The Playful Active Urban Living (PAUL) app is among the first to include 2 types of JIT prompts: JIT adaptive reminder messages to initiate a run or walk and JIT strength exercise prompts during a walk or run (containing location-based instruction videos). This paper reports on the feasibility of the PAUL app and its JIT prompts.Objective: The main objective of this study was to examine user experience, app engagement, and users' perceptions and opinions regarding the PAUL app and its JIT prompts and to explore changes in the PA behavior, intrinsic motivation, and the perceived capability of the PA behavior of the participants.Methods: In total, 2 versions of the closed-beta version of the PAUL app were evaluated: a basic version (Basic PAUL) and a JIT adaptive version (Smart PAUL). Both apps send JIT exercise prompts, but the versions differ in that the Smart PAUL app sends JIT adaptive reminder messages to initiate running or walking behavior, whereas the Basic PAUL app sends reminder messages at randomized times. A total of 23 participants were randomized into 1 of the 2 intervention arms. PA behavior (accelerometer-measured), intrinsic motivation, and the perceived capability of PA behavior were measured before and after the intervention. After the intervention, participants were also asked to complete a questionnaire on user experience, and they were invited for an exit interview to assess user perceptions and opinions of the app in depth.Results: No differences in PA behavior were observed (Z=-1.433; P=.08), but intrinsic motivation for running and walking and for performing strength exercises significantly increased (Z=-3.342; P<.001 and Z=-1.821; P=.04, respectively). Furthermore, participants increased their perceived capability to perform strength exercises (Z=2.231; P=.01) but not to walk or run (Z=-1.221; P=.12). The interviews indicated that the participants were enthusiastic about the strength exercise prompts. These were perceived as personal, fun, and relevant to their health. The reminders were perceived as important initiators for PA, but participants from both app groups explained that the reminder messages were often not sent at times they could exercise. Although the participants were enthusiastic about the functionalities of the app, technical issues resulted in a low user experience.Conclusions: The preliminary findings suggest that the PAUL apps are promising and innovative interventions for promoting PA. Users perceived the strength exercise prompts as a valuable addition to exercise apps. However, to be a feasible intervention, the app must be more stable.
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Abstract Aims: Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, these vignettes tend to oversimplify the complex relationship between patient characteristics and medical conditions, leading to biased and potentially harmful perspectives among students. Displaying aspects of patient diversity, such as ethnicity, in written cases proves challenging. Additionally, creating these cases places a significant burden on teachers in terms of labour and time. Our objective is to explore the potential of artificial intelligence (AI)-assisted computer-generated clinical cases to expedite case creation and enhance diversity, along with AI-generated patient photographs for more lifelike portrayal. Methods: In this study, we employed ChatGPT (OpenAI, GPT 3.5) to develop diverse and inclusive medical case vignettes. We evaluated various approaches and identified a set of eight consecutive prompts that can be readily customized to accommodate local contexts and specific assignments. To enhance visual representation, we utilized Adobe Firefly beta for image generation. Results: Using the described prompts, we consistently generated cases for various assignments, producing sets of 30 cases at a time. We ensured the inclusion of mandatory checks and formatting, completing the process within approximately 60 min per set. Conclusions: Our approach significantly accelerated case creation and improved diversity, although prioritizing maximum diversity compromised representativeness to some extent. While the optimized prompts are easily reusable, the process itself demands computer skills not all educators possess. To address this, we aim to share all created patients as open educational resources, empowering educators to create cases independently.
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This study shows how learner initiatives are taken during classroom discussions where the teacher seeks to make room for subjectification. Using Conversation Analysis, subjectification can be observed when students take the freedom to express themselves as subjects through learner initiatives. Drawing on data from classroom discussions in language and literature lessons in the mother tongue, the authors find that learner initiatives can be observed in three different ways: agreement, request for information, counter-response. A learner initiative in the form of an agreement appears to function mostly as a continuer and prompts the previous speaker to reclaim the turn, while the I-R-F structure remains visible. In contrast, making a request for information or giving a counter-response ensures mostly a breakthrough of the I-R-F-structure and leads to a dialogical participation framework in which multiple students participate. Findings illustrate that by making a request for information or giving a counter-response, students not only act as an independent individual, but also encourage his peers to do so.
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Preschool children's vocabulary mainly develops verbal through interaction. Therefore, the technology-enhanced storytelling (TES) activity Jeffy's Journey is developed to support parent–child interaction and vocabulary in preschool children. TES entails shared verbal storytelling supported by a story structure and real-time visual, auditory and textual prompts on a tablet computer. In this exploratory study, we investigated how TES influenced parent–child interaction and vocabulary. An experimental pretest-intervention-posttest design was followed with 44 3-year-old children and their parents in the experimental group and 27 peers in the control group. Results revealed that TES stimulated active child involvement and generated parent–child interaction, yet a great variety in TES characteristics both in time spent and usage of prompts was found among participants. Dyads that spent more time on story phases showed more and higher quality parent–child interaction. The usage of prompts was associated with improved parent–child interaction quality. Finally, an effect of TES was evidenced on children's productive vocabulary knowledge. To conclude, this study demonstrates that TES can be considered as a promising context for fostering parent–child interaction and children's vocabulary development.
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Building on the Minds-On project, this study developed the online module “Celestial Bodies” to enhance hands-on and minds-on learning, providing students with individualised feedback prompts to monitor and identify weaknesses in their understanding. The lesson centred on classifying 14 celestial bodies based on three properties, with the guidance of the online module and a map and cards. This study aimed to (1) enhance student engagement with the software, and (2) asses the impact of guided instructions and feedback prompts. We introduce our interactive lesson, present findings, and discuss their benefits in upper primary education classes to enhance student engagement, concept learning, emphasising enhanced integration of minds-on and hands-on activities.
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In dit artikel laten we zien hoe je de premiumversie van ChatGPT effectief kan inzetten voor het opstellen van een kwaliteitsverbeterplan. We delen praktische tips om de output te optimaliseren en bieden concrete voorbeelden van prompts en suggesties voor verdere verfijning. Op deze manier kan iedereen, ongeacht zijn of haar achtergrond, met vertrouwen aan de slag op het gebied van kwaliteitsverbetering.
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In competence-based vocational education, personal professional theories, in which students integrate different types of knowledge and beliefs, are seen as important. Exactly how these theories can be measured is the main focus of this study, which uses a multi-method triangulation approach, an interview and a self-report. The latter (less-structured) matter seems to provide less insight into personal professional theories then the structured methods. Both structure and adequate prompts are important when personal professional theories are explicated.
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Background: Introduction: Physical activity is essential in preventing and treating age-related chronic diseases and mortality. Retirement is a key period to promote health behaviours, as individuals restructure their routines. Thus, we aimed to identify effective components and behaviour change techniques (BCTs) in interventions promoting physical activity in retirement-age individuals. Methods: We conducted a meta-analysis. Included studies were randomised controlled trials that (p)targeted retirement-age adults (50–70 years), (i)applied BCTs, (c)had any comparator, and (o)promoted physical activity. Screening, full-text review, and data extraction were conducted independently by at least two reviewers. A multilevel random effects model with three effect sizes was fitted, and meta-regressions tested several moderators. Results: 67 studies (N = 12,147) were included. High risk of bias related to larger effects, so these studies were excluded from the main analyses. While individual effects were often non-significant, the overall pooled effect was small but statistically significant. Predictors varied across effect sizes and included action planning, motivational interviewing, and prompts/cues. Email and website delivery were associated with smaller effect sizes. Conclusions: The effectiveness of lifestyle interventions is heterogeneous and presented small effects; implementing action planning, motivational interviewing, and prompts could improve the effectiveness. However, many BCTs that are not frequently used remain unexplored.
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Prompt design can be understood similarly to query design, as a prompt aiming to understand cultural dimensions in visual research, forcing the AI to make sense of ambiguity as a way to understand its training dataset and biases ( Niederer, S. and Colombo, G., ‘Visual Methods for Digital Research’). It moves away from prompting engineering and efforts to make “code-like” prompts that suppress ambiguity and prevent the AI from bringing biases to the surface. Our idea is to keep the ambiguity present in the image descriptions like in natural language and let it flow through different stages (degrees) of the broken telephone dynamics. This way we have less control over the result or selection of the ideal result and more questions about the dynamics implicit in the biases present in the results obtained.Different from textual or mathematical results, in which prompt chains or asking the AI to explain how it got the result might be enough, images and visual methods assisted by AI demand new methods to deal with that. Exploring and developing a new approach for it is the main goal of this research project, particularly interested in possible biases and unexplored patterns in AI’s image affordances.How could we detect small biases in describing images and creating based on descriptions when it comes to AI? What exactly do the words written by AI when describing an image stand for? When detecting a ‘human’ or ‘science’, for example, what elements or archetypes are invisible between prompting, and the image created or described?Turning an AI’s image description into a new image could help us to have a glimpse behind the scenes. In the broken telephone game, small misperceptions between telling and hearing, coding and decoding, produce big divergences in the final result - and the cultural factors in between have been largely studied. To amplify and understand possible biases, we could check how this new image would be described by AI, starting a broken telephone cycle. This process could shed light not just into the gap between AI image description and its capacity to reconstruct images using this description as part of prompts, but also illuminate biases and patterns in AI image description and creation based on description.It is in line with previous projects on image clustering and image prompt analysis (see reference links), and questions such as identification of AI image biases, cross AI models analysis, reverse engineering through prompts, image clustering, and analysis of large datasets of images from online image and video-based platforms.The experiment becomes even more relevant in light of the results from recent studies (Shumailov et al., 2024) that show that AI models trained on AI generated data will eventually collapse.To frame this analysis, the proposal from Munn, Magee and Arora (2023) titled Unmaking AI Imagemaking introduces three methodological approaches for investigating AI image models: Unmaking the ecosystem, Unmaking the data and Unmaking the outputs.First, the idea of ecosystem was taken for these authors to describe socio-technical implications that surround the AI models: the place where they have been developed; the owners, partners, or supporters; and their interests, goals, and impositions. “Research has already identified how these image models internalize toxic stereotypes (Birnhane 2021) and reproduce forms of gendered and ethnic bias (Luccioni 2023), to name just two issues” (Munn et al., 2023, p. 2).There are also differences between the different models that currently dominate the market. Although Stable Diffusion seems to be the most open due to its origin, when working with images with this model, biases appear even more quickly than in other models. “In this framing, Stable Diffusion becomes an internet-based tool, which can be used and abused by “the people,” rather than a corporate product, where responsibility is clear, quality must be ensured, and toxicity must be mitigated” (Munn et al., 2023, p. 5).To unmaking the data, it is important to ask ourselves about the source and interests for the extraction of the data used. According to the description of their project “Creating an Ad Library Political Observatory”, “This project aims to explore diverse approaches to analyze and visualize the data from Meta’s ad library, which includes Instagram, Facebook, and other Meta products, using LLMs. The ultimate goal is to enhance the Ad Library Political Observatory, a tool we are developing to monitor Meta’s ad business.” That is to say, the images were taken from political advertising on the social network Facebook, as part of an observation process that seeks to make evident the investments in advertising around politics. These are prepared images in terms of what is seen in the background of the image, the position and posture of the characters, the visible objects. In general, we could say that we are dealing with staged images. This is important since the initial information that describes the AI is in itself a representation, a visual creation.
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