User Goals Research Articles

Cross-domain coreference modeling in dialogue state tracking with prompt learning

Dialogue state tracking (DST) is to identify user goals from dialogue context and it is an essential component in task-oriented dialogue systems. In multi-domain task-oriented dialogues, a user often refers to the information mentioned in the previous context when the topic is transferred to another domain. Accurate modeling of the cross-domain coreference plays an important role in building qualified DST systems. As far as we know, there is only one work attempting to model the cross-domain coreference phenomenon in DST. However, it still has low efficiency and suffers from the difficulty of handling the complex reasoning over the dialogue context. To tackle these problems, in this paper, we propose a simple but effective DST model, called Coref-DST, to track the cross-domain coreference slots. Instead of predicting the actual values via complex reasoning, Coref-DST directly identifies the coreferred domains and slots from the dialogue context. Moreover, a domain-specific prompt method is proposed to predict all the slot values in a domain simultaneously, so as to better capture the relationship among the slots. The extensive experimental results on the MultiWOZ 2.3 dataset demonstrate that Coref-DST not only outperforms the state-of-the-art DST baselines but also has higher efficiency of training and inference.

Penerapan Goal Directed Design dalam Perancangan Ulang User Interface pada Admin Marketplace BUILD ID

Build Id is a website and application-based marketplace that provides various construction needs, both products and services. The Build Id design by PT Semen Baturaja Tbk is aimed at the community as a form of implementing digital transformation in Indonesia. But the design process, to requires evaluation and improvement before it is inaugurated. This is done to avoid complaints, which can reduce the performance of applications and companies. This study aims to help design the Build Id user interface so that it is more focused on the features to be implemented so that the results can match the user's needs. In testing, this study uses the Goal Directed Design method which focuses on designing application designs by identifying user goals and needs. The results of the test prove that the Build Id marketplace for Admins is running well and as expected.

ENHANCING PRAGMATIC KNOWLEDGE WITH CHATGPT: BENEFITS AND CONSIDERATIONS

Pragmatic knowledge, or the ability to use language appropriately in social situations, is a crucial aspect of effective communication. ChatGPT, a chatbot based on the GPT-3 language model, has the potential to serve as a tool for improving pragmatic knowledge. ChatGPT is trained on a large dataset of human conversation and can generate responses that are coherent, diverse, and context-specific. By providing opportunities for users to practice their language use in a variety of contexts and real-time feedback, ChatGPT can help language learners to develop their pragmatic skills. However, the use of ChatGPT also raises several challenges and ethical considerations. Understanding the benefits and challenges of using ChatGPT for pragmatic language learning will be crucial in determining its potential as a tool for language learning. The purpose of this paper is to evaluate the benefits and challenges of using ChatGPT for pragmatic language learning. To understand the potential of ChatGPT for pragmatic language learning, the researchers review the main features and capabilities of ChatGPT. This includes its ability to generate a response in different styles and registers and how it can be used for pragmatic language acquisition. The challenges and limitations of ChatGPT are also examined, including its imperfect simulation of human conversation, reliance on large amounts of data, and potential to reinforce stereotypes or biases. The researchers discuss the potential benefits of ChatGPT, including its ability to simulate natural conversation, generate diverse and context-specific responses, and adapt to the user's language level and learning goals. In addition, to providing valuable practice in simulating natural conversation, ChatGPT’s ability to generate coherent and context-specific responses can also help the user to develop their pragmatic skills, such as understanding and interpreting the intended meanings of others. The researchers consider the ethical implications of using ChatGPT for pragmatic language learning and the need for further research in this area. Overall, this paper provides a nuanced perspective on the potential and limitations of ChatGPT as a tool for enhancing pragmatic knowledge.

A novel continual reinforcement learning-based expert system for self-optimization of soft real-time systems

Virtual globes are soft real-time systems, which stream multi-resolution data sets and render world-scale landscapes in real-time. Such systems require an adaptation mechanism to fulfill user and quality of experience (QoE) requirements. Configuration and tuning of parameters to optimize system performance is challenging due to run-time uncertainties. In this work, we propose a continual reinforcement learning-based expert system for self-optimization of a soft real-time system to meet user and quality objectives. The proposed system is capable of learning continuously by utilizing the previously learned knowledge to fulfill adaptation requirements. The proposed managing system interacts with a managed system and evolves a knowledge base of adaptation rules for run-time optimization of the managed system to meet user and quality objectives. We devise a learning classifier system for the evolution, storage, and transfer of adaptation rules using a code fragments-based rich encoding scheme. We transform user goals, system states, and execution environment conditions into observable states. We validated the proposed system for the same and cross-domain knowledge transfer and reuse scenarios for different user goals and execution environments. The results of the proposed system are compared with baseline and state-of-the-art methods where the proposed system outperformed the methods in terms of adaptation accuracy.

A Study on User-Centered Brand Design from the context of Experience Design: Focused on Pet Supplies Industry in China

The pet supplies industry in China has experienced rapid growth in recent years, leading to intensified market competition. Consequently, brands are in need of a new dimension of experience that focuses on understanding user needs and issues while building brand loyalty and trust. This study proposes a user-centered brand design model consisting of three phases: user needs discovery, issue definition, and scenario development. Employing the MEOWMO brand and the E-MOW value system as a case study, the model combines quantitative and qualitative research methods, including surveys and user experience scenarios, to help brands precisely define user needs and convey brand value. The research identifies a trend where users tend to trust brands that align with their psychological cognition and concepts. Therefore, constructing user experience scenarios aids in recognizing user expectations, goals, and issues, encouraging user participation in co-creation and ultimately enhancing brand loyalty. This model provides brands with a new direction to bolster their impact and user loyalty in the pet supplies industry. It also offers theoretical support for user-centered brand design, aiding brands in survival and value enhancement in a fiercely competitive market.

Commodification of the Subject in Presentations of Virtual Sexual Identity

When the profiles created in social networks and the content produced are examined, the problem of commodification of the subject, especially in sexual identity presentations, draws attention. For this reason, in this study, emotions, thoughts, values and situations that cause objectification of people were investigated. This research has four aims: to create a conceptual framework regarding the sexuality perception of obscene content producers in social media, to investigate the motivations that cause people to create obscene content, to identify common points that affect the network preferences of content producers and to contribute to the literature on social media studies. For this purpose, case analysis method, one of the qualitative research methods, was used. The data were collected from social media applications named Instagram, Tender, Twitter by using purposeful criterion sampling method. Users who stand out with their sexual identity presentation on these platforms were asked questions in four different categories. These categories are users' opinions about the phenomenon of sexuality, the reasons for their social network preferences, why they prefer the presentation of sexual identity (common goals) and the problems they experience on these platforms. The findings of the research are as follows: The phenomenon of sexuality is seen as an escape from real life, freedom, pleasure, communication tools and discovery. The reasons why the participants prefer social networks are the ease of access, ease of sharing, the fact that these applications carry less risk than in real life, and the advantage of control. The common goals of users are economic freedom, recognition and self-realization. Problems arising from the presentation of sexual identity are disconnection from real life, exclusion, harassment and depression. Our results clearly show that sexual identity presentations created through social media applications commodify the individual and individual values. We think that our results will contribute to the current literature and will be good ground for new research to be conducted.

Assessment of the Influence of Contextual Issues in Evaluating eHealth Systems Usability in Tanzania

The adoption of eHealth systems in healthcare facilities has rapidly increased in many countries. However, the main challenge has been the quality and level of usability of those adopted systems, and the complexity of the challenge varies from one country to another based on contextual issues. Usability is also linked to contextual issues, which have a direct impact on deciding the methods for providing services on similar systems. This research focuses on assessing the influence of contextual issues on metrics that are applicable for evaluating the usability of eHealth systems through a case study of Tanzania. The findings of this research were obtained through the quantitative method, and the analysis was performed through structural equation modelling (SEM) using AMOS and SPSS applications. It is evident from this research that contextual factors, such as user characteristics, goals and tasks, technical environment, physical environment, and resources and technology, significantly impact the usability metrics that are important to the evaluation of eHealth systems. The usability metrics for eHealth systems identified in this research include navigation visibility, accessibility, perceived ease of use, error correction, internal collaboration, information quality, external collaboration, technical quality, guide and support, and perceived benefits. This study also revealed that the usability evaluation of eHealth systems should consider both common metrics and specific metrics in order to uncover both general information system and health context-specific usability issues

Human Factors Engineering and Usability as Key Drivers for Design of Device Suited for Urine Filtration and Enrichment

Evidence-based practices (EBPs) are not always straightforward to implement in a design process, as impacts can be difficult to assess in practice and appropriate methods are not yet established. User-centered design (UCD) offers opportunities to improve implementation of EBPs. UCD refers to the use of rigorous and validated engineering tools to design technology interfaces focused on user goals, such as device safety, effectiveness, efficiency, and user satisfaction. UCD has the potential to improve six core elements of patient-centered care: (i) education and shared knowledge, (ii) free flow of information, (iii) patient engagement, (iv) teamwork, (v) attention to non-medical aspects of care, and (vi) respect for patient preferences. Technological advances in medicine over the past few decades have provided patients with a variety of inventions, such as electronic health record patient portals, home-based medical devices, and smartphone apps. Effective application of UCD principles results in patient-centered inventions that are more likely to ease patients’ treatment burden, improve their care experience, increase patient engagement, and enable patients to become more self-sufficient. Here, the URICH device design process is used as case-example to demonstrate the inclusion of usability testing and all users as early as possible in the product design and development process.

(Digital Presentation) Application of Plasma Cleaning in the Preservation and Restoration of Cultural Heritage

The chemical reactivity of metals at inappropriate climate conditions can be a threat to metallic cultural heritage. It is known that oxygen plays an important role in the corrosion and decay of metallic objects. Oxide formation is among the most prevalent destructive phenomena in this field. The metallic archaeological objects lose their original shape due to the oxide layer that develops on their surface. Among physical-mechanical and chemical techniques used in the past to remove the oxide layer from these antiques, Cold plasma technology has been proposed as a novel safe method to successfully achieve this goal. In a plasma medium, which can be formed by an electrical discharge, chemically reactive species exhibit a unique behavior in response to the injected gas type, electrical and chemical parameters, etc. This special behavior aids in achieving the user's goal through physicochemical phenomena. In this study, Hydrogen gas, with its reductive character, in the plasma phase provides free radicals of Hydrogen reacting to the copper oxide layer on ancient coins. As a result of this plasma reduction reaction, separation of the oxide layer from the copper surface is achievable. This phenomenon which is called Plasma cleaning, is a type of etching. Plasma cleaning does not destroy surface structure and information contained within metallic heritage artifacts. The feasibility of this non-destructive cleaning method has been investigated for the preservation and restoration of ancient copper coins dating back roughly 300 years ago. These ancient copper coins were exposed to a cold hydrogen plasma for 120 minutes. Results show that Tenorite and Spertiniite formed on the surface of coins have been successfully removed. The black color of Tenorite, a type of copper oxide, gives a dark appearance to copper coins' surface, and Spertitnite, as a copper hydroxide, exhibits a blue-green color on copper coins. According to the results, after 2 hours of hydrogen plasma treatment, not only these green spots have been removed, but also the dark appearance of the entire surface of the coin has been reduced. This amount of clearance is a good result considering the untreated area. Moreover, Figure 1 illustrates the coin's surface using SEM (A) before and (B) after Hydrogen plasma treatment. These images were obtained at a 200x magnification from the surface. In contrast to Figure 1(A), which had heavily polluted surfaces, case B's dark pollution has been removed, reducing the amount of darkness on the coin's surface. This study shows the efficacy of Plasma cleaning for the preservation and restoration of ancient copper coins. Figure 1

Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems

Task-oriented dialogue systems (TDSs) are assessed mainly in an offline setting or through human evaluation. The evaluation is often limited to single-turn or is very time-intensive. As an alternative, user simulators that mimic user behavior allow us to consider a broad set of user goals to generate human-like conversations for simulated evaluation. Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capabilities. Moreover, the evaluation of user simulators is an open challenge. In this work, we propose a metaphorical user simulator for end-to-end TDS evaluation, where we define a simulator to be metaphorical if it simulates a user’s analogical thinking in interactions with systems. We also propose a tester-based evaluation framework to generate variants, i.e., dialogue systems with different capabilities. Our user simulator constructs a metaphorical user model that assists the simulator in reasoning by referring to prior knowledge when encountering new items. We estimate the quality of simulators by checking the simulated interactions between simulators and variants. Our experiments are conducted using three TDS datasets. The proposed user simulator demonstrates better consistency with manual evaluation than an agenda-based simulator and a seq2seq model on three datasets; our tester framework demonstrates efficiency and has been tested on multiple tasks, such as conversational recommendation and e-commerce dialogues.

Understanding the social inclusion needs of people living in mental health supported accommodation

ObjectivesTo identify the social inclusion needs that were (i) most commonly identified and (ii) most and least commonly prioritised as support planning goals for mental health service users living in supported accommodation, using the online Social Inclusion Questionnaire User Experience (SInQUE). We qualitatively examined mental health supported accommodation staff and servicer users’ views on barriers to offering support with two less commonly prioritised areas: help finding a partner and feeling less lonely.MethodsAnonymous SInQUE data were collected during a completed study in which we developed and tested the online SInQUE. Four focus groups were conducted with mental health supported accommodation staff (N = 2) and service users (N = 2).ResultsThe most common social inclusion needs identified by service users (N = 31) were leisure activities, finding transport options, and feeling less lonely. Of the needs identified, those that service users and staff least frequently prioritised as support planning goals were having company at mealtimes, getting one’s own furniture, feeling less lonely, help with finances, and help finding a partner. In the focus groups, staff and service users identified barriers to helping with loneliness and finding a partner which related to staff and service users themselves, supported accommodation services, and wider societal factors.

Life Cycle Cost Analysis of Pumping System through Machine Learning and Hidden Markov Model

The pumping system is a critical component in various industries and consumes 20% of the world’s energy demand, with 25–50% of that energy used in industrial operations. The primary goal for users of pumping systems is to minimise maintenance costs and energy consumption. Life cycle cost (LCC) analysis is a valuable tool for achieving this goal while improving energy efficiency and minimising waste. This paper aims to compare the LCC of pumping systems in both healthy and faulty conditions at different flow rates, and to determine the best AI-based machine learning algorithm for minimising costs after fault detection. The novelty of this research is that it will evaluate the performance of different machine learning algorithms, such as the hybrid model support vector machine (SVM) and the hidden Markov model (HMM), based on prediction speed, training time, and accuracy rate. The results of the study indicate that the hybrid SVM-HMM model can predict faults in the early stages more effectively than other algorithms, leading to significant reductions in energy costs.

Staff perceptions of factors affecting the use of RAS-DS to support collaborative mental health practice

BackgroundThe Recovery Assessment Scale: Domains and Stages (RAS-DS) was designed to be both a recovery outcome measure and a tool to enhance service-user control over their recovery journey. While extensively and globally used in mental health services for the former purpose, routine use for the latter purpose is yet to be realised. The aim of this study was to identify barriers, facilitators and additional supports needed for RAS-DS to be used to support service user participation, goal setting and recovery action planning.MethodsAn online survey was conducted of mental health workers who had engaged with RAS-DS, including fixed choice and open-ended questions. Data were analysed using descriptive statistics and interpretive content analysis respectively.ResultsThe 65 respondents reported more frequent use of RAS-DS as an outcome measure than as a collaboration tool and more than half reported difficulties in using it in this way. Factors that they described as influencing the use of RAS-DS as a tool for collaboration and support included: previous experiences with RAS-DS; organisational supports and policies; awareness of the RAS-DS amongst colleagues; RAS-DS related training and support; staff time and capacity; the format of RAS-DS; service user population or context; and respondents’ own active efforts.ConclusionsExtending the use of RAS-DS, an already widely used tool, to routinely support recovery-oriented practice has both efficiency and service user empowerment benefits. However further work is needed to enable this including: provision of co-designed, accessible training resources; a user platform including built in guidance; and strategies to promote management understanding and valuing of the enhanced recovery-orientation opportunities inherent in RAS-DS use.

Augmented reality participatory platform: A novel digital participatory planning tool to engage under-resourced communities in improving neighborhood walkability

Engaging communities and utilizing local knowledge can alleviate the disparities faced by under-resourced neighborhoods with regard to providing sufficient opportunities for walkability. Digital technologies, and in particular smartphones, have created new opportunities to improve community engagement by expanding the inclusion of diverse stakeholders in decision-making. This paper introduces a novel digital participatory planning tool called the Augmented Reality Participatory Platform (ARPP). The platform uses mobile augmented reality (mobile AR) to engage residents, particularly in under-resourced communities, in identifying the design improvements necessary to enhance neighborhood walkability. ARPP consists of a mobile AR app, cloud database, and real-time mapping service. It provides a platform for two-way communication between residents of these communities and decision-makers, with potential benefits for both groups. ARPP enables residents to interact with street features and walkable design interventions in situ, generate walking-related spatial data, and share them and possible design solutions with decision-makers. The platform also assists decision-makers with presenting walkable street design alternatives developed based on community feedback to residents. To determine if ARPP is useful and user-friendly, experts and a group of residents from an under-resourced community evaluated the usability of the mobile AR app. We found that the app largely met the usability goals, and after we addressed must-solve issues identified by the experts, community participants faced very few issues when interacting with it. With recent technological advances in mobile AR and its potential for planning and design, integrating mobile AR into participatory planning is timely.

Driving the Technology Value Stream by Analyzing App Reviews

An emerging feature of mobile application software is the need to quickly produce new versions to solve problems that emerged in previous versions. This helps adapt to changing user needs and preferences. In a continuous software development process, the user reviews collected by the apps themselves can play a crucial role to detect which components need to be reworked. This paper proposes a novel framework that enables software companies to drive their technology value stream based on the feedback (or reviews) provided by the end-users of an application. The proposed end-to-end framework exploits different Natural Language Processing (NLP) tasks to best understand the needs and goals of the end users. We also provide a thorough and in-depth analysis of the framework, the performance of each of the modules, and the overall contribution in driving the technology value stream. An analysis of reviews with sixteen popular Android Play Store applications from various genres over a long period of time provides encouraging evidence of the effectiveness of the proposed approach.

Machine and deep learning for modelling heat-health relationships

Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between temperature-related and air pollution predictors. Artificial intelligence (AI) methods, which have gained popularity for health applications in recent years, can account for these complex and non-linear interactions, but have been underutilized in modelling heat-related health impacts. In this paper, six machine and deep learning models were considered to model the heat-mortality relationship in Montreal (Canada) and compared to three statistical models commonly used in the field. Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Single- and Multi-Layer Perceptrons (SLP and MLP), Long Short-Term Memory (LSTM), Generalized Linear and Additive Models (GLM and GAM), and Distributed Lag Non-Linear Model (DLNM) were employed. Heat exposure was characterized by air temperature, relative humidity and wind speed, while air pollution was also included in the models using five pollutants. The results confirmed that air temperature at lags of up to 3 days was the most important variable for the heat-mortality relationship in all models. NO2 concentration and relative humidity (at lags 1 to 3 days) were also particularly important. Ensemble tree-based methods (GBM and RF) outperformed other approaches to model daily mortality during summer months based on three performance criteria. However, a partial validation during two recent major heatwaves highlighted that non-linear statistical models (GAM and DLNM) and simpler decision tree may more closely reproduce the spike of mortality observed during such events. Hence, both machine learning and statistical models are relevant for modelling heat-health relationships depending on the end user goal. Such extensive comparative analysis should be extended to other health outcomes and regions.

Achieving trust in health-behavior-change artificial intelligence apps (HBC-AIApp) development: A multi-perspective guide

ObjectiveTrust determines the success of Health-Behavior-Change Artificial Intelligence Apps (HBC-AIApp). Developers of such apps need theory-based practical methods that can guide them in achieving such trust. Our study aimed to develop a comprehensive conceptual model and development process that can guide developers how to build HBC-AIApp in order to support trust creation among the app's users. MethodsWe apply a multi-disciplinary approach where medical informatics, human-centered design, and holistic health methods are integrated to address the trust challenge in HBC-AIApps. The integration extends a conceptual model of trust in AI developed by Jermutus et al., whose properties guide the extension of the IDEAS (integrate, design, assess, and share) HBC-App development process. ResultsThe HBC-AIApp framework consists of three main blocks: (1) system development methods that study the users' complex reality, hence, their perceptions, needs, goals and environment; (2) mediators and other stakeholders who are important for developing and operating the HBC-AIApp, boundary objects that examine users' activities via the HBC-AIApp; and (3) the HBC-AIApp's structural components, AI logic, and physical implementation. These blocks come together to provide the extended conceptual model of trust in HBC-AIApps and the extended IDEAS process. DiscussionThe developed HBC-AIApp framework drew from our own experience in developing trust in HBC-AIApp. Further research will focus on studying the application of the proposed comprehensive HBC-AIApp development framework and whether applying it supports trust creation in such apps.

Study of the Exoskeletons Lifespanin the Intensive Care Units

Abstract The exoskeletons surface as useful devices in alleviating the Intensive Care Units (ICU) medical stuff’s quality of life. A goal of manufacturers and users of exoskeletons is to determine the probability of proper functioning and increase their lifespan. Considering the extremely small number of exoskeletons for which the durations (cycles) of operation are known, in order to be able to make predictions on the characteristics of the average durations of operation, we used the bootstrap method. Through this method, starting from a small number of data collected from the exploitation of exoskeletons, a desired number of data "similar" to real data is obtained. We used the data of 10 exoskeletons of the same type and their operating times with which 2000 replicates were generated. The data were processed using the EasyFit program and the functional probabilities of the exoskeletons were deduced. A pilot study was performed in order to find the usefulness of using a passive or active torso support exoskeleton in the ICU for prone positioning. It demonstrated a lower degree of fatigue in the subjects who used the exoskeletons.

Unconscious Frustration: Dynamically Assessing User Experience using Eye and Mouse Tracking

Eye-tracking has become easier to deploy in user experience (UX) studies to get a sense of where users attend to during interactions. Additionally, mouse tracking grants insights into the cognition driving the user's behaviours and end goals, as can measuring the coordination between the eye and mouse-cursor. We created a menu navigation task based on a popular video game to assess two populations: a local cohort, and a remote cohort. We used two different eye trackers (monitor-mounted hardware, and a webcam-based algorithm; local used both simultaneously, remote used webcam only) with concurrent mouse tracking to detect friction in the UX. We found that both eye trackers had similar performance and revealed a previously undetected friction point. We argue this friction point was only detected because of the use of quantified, coordinated unconscious behaviours (eye and hand movements). The methods demonstrated are easily integrated into current UX studies with minimal cost.

The Teach-ABI Professional Development Module for Educators About Pediatric Acquired Brain Injury: Mixed Method Usability Study

Acquired brain injury (ABI) is a leading cause of death and disability in children and can lead to lasting cognitive, physical, and psychosocial outcomes that affect school performance. Students with an ABI experience challenges returning to school due in part to lack of educator support and ABI awareness. A lack of knowledge and training contribute to educators feeling unprepared to support students with ABI. Teach-ABI, an online professional development module, was created to enhance educators' ABI knowledge and awareness to best support students. Using a case-based approach, Teach-ABI explains what an ABI is, identifies challenges for students with ABI in the classroom, discusses the importance of an individualized approach to supporting students with ABI, and describes how to support a student with an ABI in the classroom. This study aims to assess the usability of and satisfaction with Teach-ABI by elementary school educators. The following questions were explored: (1) Can elementary school teachers use and navigate Teach-ABI?, (2) Are the content and features of Teach-ABI satisfactory?, and (3) What modifications are needed to improve Teach-ABI? Elementary school educators currently employed or in training to be employed in Ontario elementary schools were recruited. Using Zoom, individual online meetings with a research team member were held, where educators actively reviewed Teach-ABI. Module usability was evaluated through qualitative analysis of think-aloud data and semistructured interviews, direct observation, user success rate during task completion, and the System Usability Scale (SUS) scores. The usability benchmark selected was 70% of participants performing more than half of module tasks independently. A total of 8 female educators participated in the study. Educators were classroom (n=7) and preservice (n=1) teachers from public (n=7) and private (n=1) school boards. In terms of task performance, more than 85% of participants (ie, 7/8) independently completed 10 out of 11 tasks and 100% of participants independently completed 7 out of 11 tasks, demonstrating achievement of the module usability goal. The average overall SUS score was 86.25, suggesting a high satisfaction level with the perceived usability of Teach-ABI. Overall, participants found Teach-ABI content valuable, useful, and aligned with the realities of their profession. Participants appreciated the visual design, organization, and varying use of education strategies within Teach-ABI. Opportunities for enhancement included broadening content case examples of students with ABI and enhancing the accessibility of the content. Validated usability measures combined with qualitative methodology revealed educators' high level of satisfaction with the design, content, and navigation of Teach-ABI. Educators engaged with the module as active participants in knowledge construction, as they reflected, questioned, and connected content to their experiences and knowledge. This study established strong usability and satisfaction with Teach-ABI and demonstrated the importance of usability testing in building online professional development modules.