In this third installment of our GenAI workshop series at DIS, we focus on ‘stopsigns’—the blockages that impede progress in design research with GenAI. These stopsigns manifest as both semantic barriers (political, social, or mental frameworks) and pragmatic hurdles (technical limitations or implementation challenges) that persist despite the rapid advancements since the GenAI boom. Such stopsigns present a productive tension—they often contain partial truths worthy of consideration while simultaneously being shortsighted in ways that prevent progression. From blanket rejection to uncritical acceptance, these barriers affect how meaningfully we engage with GenAI’s potential. Our workshop welcomes both returning and first-time participants to share their experiences with these persistent challenges and work together to develop practical solutions. Through analysis of real cases and hands-on activities, we’ll build strategies for moving beyond these obstacles while acknowledging their legitimate concerns. Our goal is to foster more thoughtful integration of GenAI in design research and practice.

INTRODUCTION: With Alzheimer's disease and related dementias (ADRD) representing an enormous public health challenge, there is a need to support individuals in learning about and addressing their modifiable risk factors (e.g., diet, sleep, and physical activity) to prevent or delay dementia onset. However, there is limited availability for evidence-informed tools that deliver both quality education and support for positive behavior change such as by increasing self-efficacy and personalizing goal setting. Tools that address the needs of Latino/a, at higher risk for ADRD, are even more scarce. METHODS: We established a multidisciplinary team to develop the Healthy Actions and Lifestyles to Avoid Dementia or Hispanos y el ALTo a la Demencia (HALT-AD) program, a bilingual online personalized platform to educate and motivate participants to modify their risk factors for dementia. Grounded in social cognitive theory and following a cultural adaptation framework with guidance from a community advisory board, we developed HALT-AD iteratively through several cycles of rapid prototype development, user-centered evaluation through pilot testing and community feedback, and refinement. RESULTS: Using this iterative approach allowed for more than 100 improvements in the content, features, and design of HALT-AD to improve the program's usability and alignment with the interests and educational/behavior change support needs of its target audience. Illustrative examples of how pilot data and community feedback informed improvements are provided. DISCUSSION: Developing HALT-AD iteratively required learning through trial and error and flexibility in workflows, contrary to traditional program development methods that rely on rigid, pre-set requirements. In addition to efficacy trials, studies are needed to identify mechanisms for effective behavior change, which might be culturally specific. Flexible and personalized educational offerings are likely to be important in modifying risk trajectories in ADRD.

The brain is an incredibly complex organ capable of perceiving and interpreting a wide range of stimuli. Depending on individual brain chemistry and wiring, different people decipher the same stimuli differently, conditioned by their life experiences and environment. This study’s objective is to decode how the CNN models capture and learn these differences and similarities in brain waves using three publicly available EEG datasets. While being exposed to a variety of media stimuli, each brain produces unique brain waves with some similarity to other neural signals to the same stimuli. However, to figure out whether our neural models are able to interpret and distinguish the common and unique signals correctly, we employed three widely used CNN architectures to interpret brain signals. We extracted the pre-processed versions of the EEG data and identified the dependency of time windows on feature learning for song and movie classification tasks, along with analyzing the performance of models on each dataset. While the minimum length snippet of 5 s was enough for the personalized model, the maximum length snippet of 30 s proved to be the most efficient in the case of the generalized model. The usage of a deeper architecture, i.e., DeepConvNet was found to be the best for extracting personalized and generalized features with the NMED-T and SEED datasets. However, EEGNet gave a better performance on the NMED-H dataset. Maximum accuracy of 69%, 100%, and 56% was achieved in the case of the personalized model on NMED-T, NMED-H, and SEED datasets, respectively. However, the maximum accuracies dropped to 18%, 37%, and 14% on NMED-T, NMED-H, and SEED datasets, respectively, in the generalized model. We achieved a 5% improvement over the state of the art while examining shared experiences on NMED-T. This marked the outof-distribution generalization problem and signified the role of individual differences in media perception, thus emphasizing the development of personalized models along with generalized models with shared features at a certain level.

This one day workshop will explore the use of Generative Artificial Intelligence (GenAI) in design research and practice. Generative technologies are developing rapidly and many designers are using them. Yet, there remains little published work on the use of GenAI in design. Our goal is to not only showcase the potential of GenAI for design, but to engage in discussions of its shortcomings and opportunities as they have been already articulated by scholars. By synthesizing both published and unpublished works, we will develop best practices, ethical considerations, and future research directions for the use of GenAI in design. We will explore a range of topics and themes, including leveraging the characteristics of GenAI for design, mapping the diverse applications of GenAI in design, envisioning a framework for design, and guiding future work on GenAI in design research. Ultimately, we hope to provide a roadmap for the integration of GenAI into the design research process and to encourage designers and researchers to explore the potential of GenAI in a thoughtful and deliberate way.

Learning engineering adds tools and processes to learning platforms to support improvement research. One kind of tool is A/B testing-common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE). A number of A/B testing systems focused on educational apps have arisen recently, including UpGrade and E-TRIALS. A/B testing can help improve educational platforms, yet challenging issues in education go beyond the generic paradigm. In response, a number of of digital learning platforms is opening their systems to learning-improvement research by instructors and/or third-party researchers, with specific supports necessary for education-specific research designs. This workshop will explore how A/B testing in educational contexts is different, how learning platforms are opening up new possibilities, and how these empirical approaches can be used to drive powerful gains in student learning. It will also discuss forthcoming opportunities for funding to conduct platform-enabled learning research.

Music recommendation systems struggle with predicting the aesthetic responses of listeners based solely on acoustic characteristics, which are dependent on the listener's perception. This research correlates acoustic music features with brain responses to report the neural aesthetic hypothesis that the intensity of an aesthetic experience can be decoded based on the degree of correlation to brain responses. We employ hybrid encoding-decoding model (Canonical Correlation Analysis) to identify music features that maximally covary with brain responses. EEG signals of 20 participants are analyzed while they listen to 12 songs and mark their enjoyment on a scale of 1 to 5. Firstly, 18 acoustic features are extracted from music signals and transformed into the first principal component (PC1). In addition, two other features used for analysis are root mean square (RMS) and Spectral Flux (Flux). The first principal canonical component (CC1) with PC1 determines significant (p<0.05) evidence of correlating with brain responses that increasing correlation reflects increased enjoyment. We consider each participant's average CC1 values and enjoyment rating over all 12 songs, followed by plotting a correlation graph to decode the relationship. We observe a significant (p<0.05) positive linear correlation with increasing CC1 scores of PC1 features against increased enjoyment rating. PC1 shows the maximum Pearson correlation (r = 0.48, p = 0.03). In addition, we segregate the brain responses based on low (1,2) and high (3,4) enjoyment ratings and find that higher CC1 values correspond to brain responses of high enjoyment and low values to low enjoyment in all three features. Our experiments reveal that Canonical correlation reflects music-induced pleasure and can be employed in EEG-enabled headphones to decode the user experience, leading to better recommendations.

Introduction: Designing artificial intelligence (AI) to support health and wellbeing is an important and broad challenge for technologists, designers, and policymakers. Drawing upon theories of AI and cybernetics, this article offers a design framework for designing intelligent systems to optimize human wellbeing. We focus on the production of wellbeing information feedback loops in complex community settings, and discuss the case study of My Wellness Check, an intelligent system designed to support the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic. Methods: The basis for our discussion is the community-led design of My Wellness Check, an intelligent system that supported the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic. Our system was designed to create an intelligent feedback loop to assess community wellbeing needs and to inform community action. This article provides an overview of our longitudinal assessment of students and staff wellbeing (n = 20,311) across two years of the COVID-19 pandemic. Results: We further share the results of a controlled experiment (n = 1,719) demonstrating the enhanced sensitivity and user experience of our context-sensitive wellbeing assessment. Discussion: Our approach to designing “AI for community wellbeing,” may generalize to the systematic improvement of human wellbeing in other human-computer systems for large-scale governance (e.g., schools, businesses, NGOs, platforms). The two main contributions are: 1) showcasing a simple way to draw from AI theory to produce more intelligent human systems, and 2) introducing a human-centered, community-led approach that may be beneficial to the field of AI.

Background: Readability metrics provide us with an objective and efficient way to assess the quality of educational texts. We can use the readability measures for finding assessment items that are difficult to read for a given grade level. Hard-to-read math word problems can put some students at a disadvantage if they are behind in their literacy learning. Despite their math abilities, these students can perform poorly on difficult-to-read word problems because of their poor reading skills. Less readable math tests can create equity issues for students who are relatively new to the language of assessment. Less readable test items can also affect the assessment's construct validity by partially measuring reading comprehension. Objectives: This study shows how large language models help us improve the readability of math assessment items. Methods: We analysed 250 test items from grades 3 to 5 of EngageNY, an open-source curriculum. We used the GPT-3 AI system to simplify the text of these math word problems. We used text prompts and the few-shot learning method for the simplification task. Results and Conclusions: On average, GPT-3 AI produced output passages that showed improvements in readability metrics, but the outputs had a large amount of noise and were often unrelated to the input. We used thresholds over text similarity metrics and changes in readability measures to filter out the noise. We found meaningful simplifications that can be given to item authors as suggestions for improvement. Takeaways: GPT-3 AI is capable of simplifying hard-to-read math word problems. The model generates noisy simplifications using text prompts or few-shot learning methods. The noise can be filtered using text similarity and readability measures. The meaningful simplifications AI produces are sound but not ready to be used as a direct replacement for the original items. To improve test quality, simplifications can be suggested to item authors at the time of digital question authoring.

Advances in neurotechnology have enhanced and simplified our ability to research brain activity with low-cost and effective equipment. One such scalable and noninvasive technique is Electroencephalography (EEG), which detects and records electrical brain activity. Brain activity recognition is one of the emerging problems as EEG wearables become more readily available. Our research has modeled EEG signals to classify three states (i) music listening, (ii) movie watching, and (iii) meditating. The datasets incorporating the brain signals induced while performing these activities are NMED-T for music listening, SEED for movie watching, and VIP_Y_HYT for meditating. EEG activity is transformed into deep representation using a convolutional neural network comprising three different types of 2D convolutions: Temporal, Spatial, and Separable, to capture dependencies and extract high-level features from the data. The Depthwise Convolution function is responsible for learning spatial filters within each temporal convolution, and combining these spatial filters across all temporal bands optimally is learned by the Separable Convolutions. EEGNet and EEGNet-SSVEP are specially designed for EEG Signal Processing and Classification, and the DeepConvNet has incorporated more convolution layers. Our finding demonstrates that increasing the number of layers in the Network provided a higher accuracy of 99.94% using DeepConvNet. In contrast, the accuracy of EEGNet and EEGNet-SSVEP resulted in 85.63% and 75.76%, respectively.

Entrainment is a phenomenon of phase or temporal matching of one system with that of another system. Human neural activity has been shown to resonate with external auditory stimuli. When we enjoy a piece of music, there is a resonance of brain responses with auditory signals. The crux of music cognition is based on this resonance of musical frequencies with intrinsic neural frequencies. It has also been demonstrated that the neural activities are synchronized across participants while listening to music, shown by high inter-subject correlation. In this work, we use this fact to predict the drumbeat a participant listens to based on their EEG response to the drumbeat. We also tested whether we could train on a smaller dataset and test with the rest of the dataset. We generated a frequency∗channel plot and fed it to a CNN model to predict drumbeat with a classification accuracy of 97% for 60-20-20 (train-dev-test) data split protocol and 94% accuracy for 20-20-60 data split. We also got 100% classification accuracy for predicting participants for both the data split protocols.

Naturalistic music typically contains repetitive musical patterns that are present throughout the song. These patterns form a signature, enabling effortless song recognition. We investigate whether neural responses corresponding to these repetitive patterns also serve as a signature, enabling recognition of later song segments on learning initial segments. We examine EEG encoding of naturalistic musical patterns employing the NMED-T and MUSIN-G datasets. Experiments reveal that (a) training machine learning classifiers on the initial 20s song segment enables accurate prediction of the song from the remaining segments; (b) β and γ band power spectra achieve optimal song classification, and (c) listener-specific EEG responses are observed for the same stimulus, characterizing individual differences in music perception.

The article provides an open-source Music Listening- Genre (MUSIN-G) EEG dataset which contains 20 participants’ continuous Electroencephalography responses to 12 songs of different genres (from Indian folk music to Goth Rock to western electronic), along with their familiarity and enjoyment ratings. The participants include 16 males and 4 females, with an average age of 25.3 (+/-3.38). The EEG data was collected at the Indian Institute of Technology Gandhinagar, India, using 128 channels Hydrocel Geodesic Sensor Net (HCGSN) and the Netstation 5.4 data acquiring software. We provide the raw and partially preprocessed data of each participant while they listened to 12 different songs with closed eyes. The dataset also contains the behavioural familiarity and enjoyment ratings (scale of 1 to 5) of the participants for each of the songs. In this article, we further discuss the preprocessing steps which can be used on the dataset and prepare the data for analysis, as in the paper [1].

One interpretation of breathing exercise is to enforce mind-body harmony, when someone feels well and healthy, different organs of our body function harmoniously. One dysfunctional organ may disturb the resonating mechanism across multiple organs. There are different breathing techniques, and recent scientific evidence encourages understanding the neural correlates of breathing. This research investigates breathing exercises at two paces: Rapid and Deep Slow using neural signals. We collect Electroencephalography (EEG) recordings of 14 participants performing breathing tasks. EEG signals are primarily decomposed in frequency bands that designate different cognitive functions. We extract six primary frequency bands, including delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), low beta (13-20 Hz), high beta (21-30 Hz), and gamma (30-40 Hz). Two different techniques are utilized to report the findings encompassing power spectral analysis and employing machine learning classifiers to discriminate features among different stages of inhalation and exhalation with the significance of different frequencies bands. Lowered beta power in Slow Deep breathing is observed compared to Rapid Breathing, which may suggest increased relaxation, calmness, and anxiety reduction. Differences between the two conditions observed in the frontoparietal cortex may be attributed to differences in voluntary control between the two tasks. We observed classification accuracy of 72 % using low beta between Rapid and Deep Slow breathing using Decision Tree. Several interesting findings are observed in different scalp regions suggesting future direction for further investigation. This study contributes to the understanding of neural signatures for different breathing practices. The implication of this research in health care is to design personalized therapies and to design better breathing mobile applications for daily use.

Human-computer interaction investigates how people learn from technology, and how they use technology in everyday life. Researchers have used brain-computer interfaces to understand how technology can be designed to support human cognition and behavior. The most famous and consumer-friendly approach to measuring brain signals is electroencephalography (EEG) due to its non-invasive, portable, relatively inexpensive, and high temporal resolution. In this study, we develop machine learning models to distinguish between the neural oscillations of meditators and non-meditators. Previous studies have used power spectrum density, entropy, and functional connectivity to distinguish various meditation traditions. We use EEG data set comprising neural activity of expert meditators of Himalayan Yoga (HYT), Vipassana (VIP), Isha Shoonya (SYN), and non-expert control subjects (CTR). We analyze the data using 13 different machine learning models for within-subject and cross-subject. We present the results for six classification conditions for both meditation and mind-wandering. Features extracted from the mean of 64 EEG time series are fed into machine learning classifiers during training. We obtain maximum accuracy for within-subject classification in both meditation and mind-wandering. In cross-subject analysis, we obtained 18.3% above chance level in meditation between control and Isha Shoonya, and similarly above 18% chance level in mind-wandering between control and Vipassana. We discuss the implications of this result for the emerging consumer EEG headset facilitating meditation practice. Our results indicate that personalized models (within-subject) and generalized models (cross-subject) could guide naive (beginner) practitioners to meditate and aim to modulate brain signals by practicing to reach the expert level.