Use Of Tool Research Articles

Design and development of an ai-driven communication model for stimulating memory recall in patients with Alzheimer’s and other neurodegenerative diseases

This study presents the development and evaluation of an AI-driven communication tool designed to stimulate memory recall in patients with Alzheimer’s disease and other neurodegenerative disorders. The tool integrates machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM) networks, with mass communication theories to deliver personalized and adaptive communication strategies. The AI models were trained and tested on patient interaction data, enabling the tool to dynamically adjust its prompts based on real-time cognitive states. Performance metrics such as accuracy, precision, and AUC-ROC were used to assess model efficacy, with the Random Forest model achieving the highest performance across all metrics. Clinical trials demonstrated significant improvements in memory recall and emotional engagement in patients using the AI tool compared to standard cognitive stimulation therapy. Caregivers also reported higher satisfaction with the tool's usability and impact on patient interaction quality. These findings highlight the potential of AI-driven, personalized communication tools to revolutionize Alzheimer's care, offering scalable, adaptive interventions that improve cognitive and emotional health. Academically, this work advances the integration of AI and health communication, emphasizing culturally sensitive and patient-centered approaches. In healthcare, it presents a promising non-pharmacological intervention that can be easily scaled for widespread use, potentially reducing caregiver burden and improving patient outcomes.

Developed Internet of Vehicles Architecture: Communication, Big Data and Route Determination Perspectives

Internet of vehicles (IoV) has become an important research topic due to its direct effect on intelligent transportation systems (ITS) development. There are many challenges in the IoV environment, such as communication, big data and best route assigning. In this paper, an effective IoV architecture is proposed. This architecture has four main objectives. The first objective is to utilize a powerful communication scheme in which three tiers of coverage tools — Internet, satellite, high-altitude platform (HAP) — are utilized. Therefore, the vehicles maintain a continuous connection to the IoV environment everywhere. The second objective is to apply filtering and prioritization mechanisms to reduce the detrimental effects of IoV big data. The third objective is to assign the best route for a vehicle after determining its real-time priority. The fourth objective is to analyze the IoV data. The proposed architecture performance is measured using a simulation environment that is created by the NS-3 package. The simulation results proved that the proposed IoV architecture has a positive impact on the IoV environment according to the performance metrics: energy, success rate of route assignment, filtering effect, data loss, delay, usage of coverage tools and throughput.

Cocreation of a Video Feedback Tool for Managing Self-Care at Home With Pairs of Older Adults: Remote Experience-Based Co-Design Study.

Involving older adults in co-design processes is essential in developing digital technologies and health care solutions to enhance self-care management at home, especially for older adults with chronic illness and their companions. Remote co-design approaches could provide technologically sustainable solutions that address their personal needs. This study aimed to cocreate and test the usability of a video feedback tool to facilitate self-care management at home. This experience-based co-design approach involved collaboration between 4 pairs of older adults, 4 researchers, and 2 service designers in three steps: (1) six iterative workshops (5 remote and 1 in person) to cocreate self-care exercises within an existing video feedback tool by identifying factors influencing self-care management; (2) developing and refining the self-care exercises based on suggestions from the older adults; and (3) usability testing of the cocreated exercises with the 4 pairs of older adults in their homes. Among the older adults (68-78 years), 3 adults had heart failure and 1 adult had hypertension. Data were analyzed inductively through thematic analysis and deductively using the USABILITY (Use of Technology to Engage in Adaptation by Older Adults and/or Those With Low or Limited Literacy) framework. The identified influencing factors guiding the contents and format development of 2 new self-care exercises were that pairs of older adults support and learn from each other in performing self-care, which increases their motivation and engagement in practicing self-care at home. The usability test of the 2 new self-care exercises, "Breathing exercises" and "Picking up from the floor," revealed that the pairs found the exercises and the video feedback component valuable for learning and understanding, for example, by comparison of performances highlighting movement variability. However, they found it difficult to manage the video feedback tool on their own, and a support structure or tailored education or training was requested. This study emphasizes that the video feedback tool holds the potential to facilitate learning and understanding in self-care management, which may support motivation. The studied video feedback tool can be beneficial for pairs of older adults managing self-care at home as a complement to traditional health care services, but an accurate supporting structure is required. The effectiveness of the video feedback tool and its integration into existing health care services still need to be assessed and improved through careful design and structured support.

The Impact of AI-Powered Personalized Learning on Student Performance in Udaipur Colleges

The integration of Artificial Intelligence (AI) into educational settings has garnered increasing attention for its potential to enhance personalized learning and improve student outcomes. This study investigates the impact of an AI-powered personalized learning tool implemented at colleges in Udaipur. Using a mixed-methods approach, we collected quantitative data through pre-test and post-test scores and qualitative feedback via surveys and interviews with students and teachers. The results revealed significant improvements in student performance and engagement, with the AI tool providing tailored learning experiences that addressed individual needs. Statistical analyses demonstrated a substantial positive effect on academic outcomes, while qualitative feedback highlighted the tool's usability and effectiveness in identifying students' weaknesses. The findings suggest that AI-powered personalized learning can be a valuable asset in education, offering recommendations for broader adoption and further research in this field.

Generative AI: Boosting Adaptability and Reducing Workplace Overload

ABSTRACT In the evolving digital work environment, the rising prevalence of generative AI tools presents a complex challenge for practitioners: deciding whether to allow or restrict their use in organizational settings. Our research contributes to expanding the broaden-and-build theory and the job demands-resources model (JD-R) within the context of generative AI tools usage. This study investigates the impact of generative AI tools on employees’ perceived work overload, focusing on the mediating role of employee adaptability. Utilizing a survey of 307 employees and Structural Equation Modeling (SEM) techniques, findings show that generative AI tools usage by employees not only directly reduces perceived work overload but also significantly boosts employee adaptability, further decreasing perceived work overload. This highlights the dual benefits of generative AI tools in the workplace, offering valuable insights for managers on consciously integrating these technologies to enhance adaptability and reduce workload stress.

User-centric AI: evaluating the usability of generative AI applications through user reviews on app stores

This article presents a usability evaluation and comparison of generative AI applications through the analysis of user reviews from popular digital marketplaces, specifically Apple’s App Store and Google Play. The study aims to bridge the research gap in real-world usability assessments of generative AI tools. A total of 11,549 reviews were extracted and analyzed from January to March 2024 for five generative AI apps: ChatGPT, Bing AI, Microsoft Copilot, Gemini AI, and Da Vinci AI. The dataset has been made publicly available, allowing for further analysis by other researchers. The evaluation follows ISO 9241 usability standards, focusing on effectiveness, efficiency, and user satisfaction. This study is believed to be the first usability evaluation for generative AI applications using user reviews across digital marketplaces. The results show that ChatGPT achieved the highest compound usability scores among Android and iOS users, with scores of 0.504 and 0.462, respectively. Conversely, Gemini AI scored the lowest among Android apps at 0.016, and Da Vinci AI had the lowest among iOS apps at 0.275. Satisfaction scores were critical in usability assessments, with ChatGPT obtaining the highest rates of 0.590 for Android and 0.565 for iOS, while Gemini AI had the lowest satisfaction rate at −0.138 for Android users. The findings revealed usability issues related to ease of use, functionality, and reliability in generative AI tools, providing valuable insights from user opinions and feedback. Based on the analysis, actionable recommendations were proposed to enhance the usability of generative AI tools, aiming to address identified usability issues and improve the overall user experience. This study contributes to a deeper understanding of user experiences and offers valuable guidance for enhancing the usability of generative AI applications.

Unusual Composition of the Sarezzano Reliquary Busts

The interdisciplinary study of two reliquary busts from Sarezzano (Piedmont, Italy) is a perfect example of the necessity to provide for material characterisation as a recurring common practice in historical studies and a mandatory step in conservation assessment. Furthermore, the diagnostics of cultural heritage play a crucial role in art historical research, providing relevant information on artefacts’ genesis, production technology, and conservation history. The study of the materials of the reliquary busts was performed by non-invasive (portable X-ray fluorescence spectrometry) and micro-invasive (stereomicroscope, attenuated total reflection Fourier transform infrared spectroscopy and powder X-ray diffraction analysis) methods. According to the results, the busts were found to be made of a tin–lead alloy, a rather unusual material for mediaeval reliquary busts. Moreover, the outcome suggests that the busts were originally silvered, except for the hair and beard which are still gilded. The analysis reveals the use of colophony as an adhesive buffer layer on the busts’ alloy, as well as inside them, to favour the metal working process, since it is found as degraded residue. Finally, even the typology of alloy decay is defined. All this information has enabled us to determine the artistic technique and estimate the value and quality of the material employed. In addition, it has led to the correct choice of materials and methods to be adopted during the restoration, and therefore the usage of more suitable solvents and tools.

Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety

BackgroundThe global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients’ needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance.PurposeThis review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients’ rights and safety.MethodsWe deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study.ResultsWe report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare.ConclusionsOur findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

Usability of a digital tool to support long-lasting insecticide net distribution in Northern Bahr el Ghazal State, South Sudan.

Long-lasting insecticidal nets (LLINs) have been a widely used malaria prevention method for decades. In South Sudan, LLINs are typically distributed by volunteers who use paper-based systems to collect distribution data. Paper-based systems are simple to use but have a higher occurrence of data inaccuracies and can hinder the timely use of data for decision-making. In 2022, a digital tool was introduced to collect data during the LLIN campaign in Northern Bahr el Ghazal (NBeG). The tool aimed to improve the accuracy of data entry and enable data to be used in real-time for decision making during the campaign. The digital tool was developed with offline functionality and interoperability with DHIS2 tracker version 2.8 in DHIS2 version 2.38. This study assessed the usability of the tool according to user perspectives. A questionnaire containing open- and closed-ended questions was conducted with users of the digital tool, supervisors and other key stakeholders in five counties of NBeG. The questionnaire was administered using Malaria Consortium's Projects Results System Android mobile application. Usability was determined through a modified and validated System Usability Scale (SUS) approach. A total of 93 participants responded to the usability questionnaire. The mean (± standard deviation) usability score across 10 SUS-scoring items was60.91 (12.87), indicating a modest level of usability. The majority of users agreed the tool was useful for managing the LLIN distribution workflow, was easy to use, reduced workload, and supported stock management and real-time campaign monitoring. There was no significant difference in the usability scores across genders, roles, and counties. Respondents with experience of both paper-based and the digital tool tended to express a preference for the digital tool over paper-based systems. The majority of respondents also reported they would recommend the digital tool to colleagues. Digital tools are perceived to improve data collection during LLIN campaigns, even in remote areas where network coverage is challenging. Additional improvements can be implemented to overcome operational challenges and improve usability of the tool. Further study is needed to assess the impact of the digital tool on data quality and real-time data use.

SAPPNet: students’ academic performance prediction during COVID-19 using neural network

A variety of reasons have made it more difficult for educators and tutors to anticipate students’ performance. Numerous researchers have used various predictive models to identify students who may be at-risk of dropping out early. Additionally, these methods were used to forecast final semester grades based on various datasets. However, these prediction models still fall short of meeting educational management requirements. In this paper, we propose the deep learning (DL) based model named students academic performance prediction network (SAPPNet) to predict the students’ grades. We consider the questionnaire-based Jordan University dataset which contains demographic information, usage of digital tools before and after COVID-19, sleep times before and after COVID-19, social interaction, psychological state, and academic performance. SAPPNet consists of spatial convolution modules which are designed to extract spatial dependencies includes categorical and numerical attributes that represent static features (gender, level/year, age, digital tools used before and after COVID-19, psychological condition using prolonged e-learning tools) and temporal module for temporal dependencies involves sequences that capture changes before and after COVID-19. Additionally, we also try to implement classical machine learning (ML) models including support vector machine, k nearest neighbor, decision tree, and random forest, and DL models named artificial neural network, convolutional neural network, long short-term memory, and students learning prediction network. Simulation results show that SAPPNet achieved the best performance compared to state-of-the-art methods, with an accuracy, precision, recall, and an F1-score of 93%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93\\%$$\\end{document}. The proposed model with spatial and temporal modules improves the prediction performance, and it implies new aspect of the educational dataset.

Conducting Patient-Oriented Research in Pediatric Populations: A Narrative Review.

It has become increasingly common for researchers to partner with patients as members of the research team and collaborate to use their lived experiences to shape research priorities, interventions, dissemination, and more. The patient-oriented research (POR) model has been adopted by both adult and pediatric health researchers. This cultural change to conducting pediatric health research brings with it new methodologies, tools, challenges, and benefits. In this review, we aim to provide guidance on how to conduct POR for pediatric populations using examples from the literature. We describe considerations for engagement before the project begins, for engagement across the research cycle, and for measurement and evaluation. We aim to show that conducting POR is feasible, beneficial, and that many common challenges and barriers can be overcome with preparation and usage of specific tools.

Evaluating User Experiences and Preferred Features of a Web-Based 24-Hour Dietary Assessment Tool: Usability Study.

Intake24, a web-based 24-hour dietary recall tool developed in the United Kingdom, was adapted for use in New Zealand (Intake24-NZ) through the addition of a New Zealand food list, portion size images, and food composition database. Owing to the customizations made, a thorough evaluation of the tool's usability was required. Detailed qualitative usability studies are well suited to investigate any challenges encountered while completing a web-based 24-hour recall and provide meaningful data to inform enhancements to the tool. This study aims to evaluate the usability of Intake24-NZ and identify improvements to enhance both the user experience and the quality of dietary intake data collected. We used a mixed methods approach comprising two components: (1) completion of a single 24-hour dietary recall using Intake24-NZ with both screen observation recordings and collation of verbal participant feedback on their experience and (2) a survey. A total of 37 participants aged ≥11 years self-completed the dietary recall and usability survey (men and boys: 14/37, 38% and women and girls: 23/37, 62%; Māori: 10/37, 27% and non-Māori: 27/37, 73%). Although most (31/37, 84%) reported that Intake24-NZ was easy to use and navigate, data from the recorded observations and usability survey revealed challenges related to the correct use of search terms, search results obtained (eg, type and order of foods displayed), portion size estimation, and associated food prompts (eg, did you add milk to your tea?). This comprehensive usability study identified challenges experienced by users in completing a dietary recall in Intake24-NZ. The results informed a series of improvements to enhance user experience and the quality of dietary data collected with Intake24-NZ, including adding new foods to the food list, optimizing the search function and ordering of search results, creating new portion size images, and providing clearer instructions to the users.

Development and Pilot Study of myfood24 West Africa—An Online Tool for Dietary Assessment in Nigeria

Background and objective: Tools to accurately and efficiently measure dietary intake in Nigeria are lacking. We aimed to develop and assess the usability of a new online dietary assessment tool for Nigeria—myfood24 West Africa. Methods: We developed the myfood24 West Africa database using data from existing food composition tables, packaged foods labels and research articles. The development followed seven steps: identified data sources, selected foods, processed/cleaned the data, calculated the nutrient content of recipes, created and allocated portion sizes, quality-checked the database and developed food accompaniments. To pilot the tool, we recruited 179 university staff in Nigeria using a cross-sectional design. Usability was assessed using a questionnaire that included the System Usability Scale (SUS) and a feedback session. Results: The database included 924 foods, with up to 54 nutrients and 35 portion-size images allocated to foods. Sixty percent of the data were sourced from the 2019 West Africa Food Composition Table, 17% from back-of-pack labels of packaged foods, 14% from the 2017 Nigerian Food Composition Table, 5% from generated recipes and 4% from the published literature. Of the participants, 30% (n = 53) self-recorded their food intake, with a total of 1345 food and drink entries from both self- and interviewer-collected data. The mean SUS score of 74 (95% CI: 68,79) indicated good usability. The feedback showed that the tool was user-friendly, educational and included a variety of local foods. Conclusions: This new tool will enhance the dietary assessment of the Nigerian population. More work will expand coverage to include more foods from the region.

Developing and evaluating an e-learning and e-assessment tool for organic chemistry in higher education

A convincing e-learning system for higher education should offer adequate usability and not add unnecessary (extraneous) cognitive load. It should allow teachers to switch easily from traditional teaching to flipped classrooms to provide students with more opportunities to learn and receive immediate feedback. However, an efficient e-learning and technology-enhanced assessment tool that allows generating digital organic chemistry tasks is yet to be created. The Universities of Bonn and Duisburg-Essen are currently developing and evaluating an e-learning and technology-enhanced assessment tool for organic chemistry. This study compares the effectiveness of traditional paper-pencil-based and digital molecule-drawing tasks in terms of student performance, cognitive load, and usability—factors that all contribute to learning outcomes. Rasch analysis, t-tests, and correlation analyses were used for evaluation, revealing that the developed system can generate digital organic chemistry tasks. Students performed equally well on simple digital and paper-pencil molecule-drawing tasks when they received an appropriate introduction to the digital tool. However, using the digital tool in two of three studies imposes a higher extraneous cognitive load than using paper and pencil. Nevertheless, the students rated the tool as sufficiently usable. A significant negative correlation between extraneous load and tool usability was found, suggesting room for improvement. We are currently concentrating on augmenting the functionality of the new e-learning tool to increase its potential for automatic feedback, even for complex tasks such as reaction mechanisms.

Identifying Differences in the Application of HR Marketing Tools in Companies in the Secondary Sector – a Case Study of Mining, Construction, and Manufacturing Industries

HR marketing is a relatively new field in corporate practice. When implemented effectively and designed professionally, HR marketing can give companies a significant competitive advantage in the labour market. Increasingly, HR managers and executives focus on securing high-quality and skilled employees who are integral to building a strong brand and employer value. To date, no studies have specifically examined HR marketing within the secondary sector, which appears to be stagnating in economically developed countries. This study aims to identify differences in the application of HR marketing tools among companies operating in the secondary sector, particularly within the mining, construction, and manufacturing industries. The research was conducted in two stages: the first stage involved an analysis of HR marketing practices among a sample of 215 companies operating in the Czech Republic and Slovakia. The second stage focused on a case study of 10 companies from the secondary sector, specifically three companies operating in the mining, construction, and manufacturing industries. This study offers a comparative analysis of HR marketing tool usage among companies within the secondary sector, including those in the Czech Republic and Slovakia. The key findings address the research questions and hypotheses. The results indicate that companies in the secondary sector primarily use traditional HR marketing tools such as company websites, personnel agency services, and job portals. Mining and manufacturing companies employ a wider range of HR marketing tools to enhance employee loyalty than construction companies.

Designing with words: exploring the integration of text-to-image models in industrial design

ABSTRACT This study investigates the effectiveness of Text-to-image (T2I) models in assisting industrial designers with the visualization of product concepts. Using action research and a reflection-in-action, this research conducts a series of experiments utilizing Stable Diffusion for generating chair concepts. The research findings are positioned within an existing framework of computational tools usage, and assess the quality of the generated images in supporting industrial design activities during the define and develop phases of the design process. While T2I models provide competent support to ambiguity and sketch improvement, their efficacy in generating coherent solutions for further development is limited. This study provides guidelines for the effective application of T2I models in industrial design practice. It discusses the T2I models’ suitability for specific design activities, addresses associated risks, and proposes a taxonomy of prompt modifiers that ensures a closer match between input prompts and generated product concepts.

University Students’ Attitudes and Perceptions towards AI Tools: Implications for Sustainable Educational Practices

The integration of artificial intelligence (AI) tools in educational settings offers significant opportunities to promote sustainability by transforming learning experiences. This study analyses the usage, attitudes, and perceptions of AI tools among university students in Slovenia providing a comprehensive analysis that informs both academic practices and policy-making with emphasis on sustainability. We used a structured questionnaire with a sample of 422 participants reflecting a diverse demographic profile across various fields of study. The questionnaire was designed to measure the frequency of AI tool usage, the purposes for which these tools are employed, and students’ attitudes and perceptions towards AI’s potential benefits and drawbacks in education. Statistical analyses, including Analysis of Variance (ANOVA), were utilized to test hypotheses concerning differences in AI tool usage based on the level and field of study. Findings reveal that students recognize the efficiency of AI, but express concerns about its impact on learning quality and academic integrity, emphasizing the need for a balanced and responsible integration of AI in education to achieve sustainable outcomes. Results indicated that a majority of students are engaging with AI tools, with varied frequencies of use largely dependent on their field of study and academic level. The findings suggest that while AI tools are becoming an integral part of the educational landscape in Slovenia, there is a critical need to address the educational, ethical, and psychological impacts of these technologies. The results highlight the necessity for further research into the educational implications of AI, suggesting a balanced and sustainable approach to integrating these technologies into higher education curricula. Such an approach ensures that the adoption of AI not only enhances learning outcomes but also aligns with the principles of sustainability, promoting long-term benefits for both education and society.

Translation and initial cross-cultural adaptation of the tool for support-gradual return-to-work for persons with chronic musculoskeletal pain to the Swedish setting.

A well-defined and clear procedure is a key factor supporting return-to-work and enhancing collaboration and understanding between employers and employees. The adaptation of the Tool for Support-Gradual Return to Work, TS-GRTW, addresses relevant cultural aspects valuable for wider adoption. develop a Swedish version, the GRTWswe, for implementation and integration into the Swedish labor market's RTW process. This involved translating, culturally adapting, and assessing the appropriateness and utility. In the initial step, a double back translation was performed to create an initial translated version. This version was then utilized in individual consultations, accompanied by an agreement questionnaire. For the subsequent step, group consultations were held to refine and customize the tool to suit the Swedish context. Ten occupational therapists completed the questionnaires, with mean agreement scores surpassing three on a four-point scale. Out of these, nine participated in group consultations. The findings suggest the requirement for specific modifications to the GRTWswe. These adaptations are essential because of cultural differences in organizational structures and reference frameworks. Moreover, participants unanimously agreed to broaden the scope of target groups, encompassing employees without regard for diagnosis and expanding the range of professions that can utilize this tool. This step aims to enhance the tool's applicability and usefulness. The study found strong alignment between questionnaire responses and group consultations outcomes, affirming the adapted tool's suitability for use in a Swedish context. The tool benefits employers and employees by enhancing communication, encouraging collaboration, and structuring processes, promising lasting improvements to work conditions.

The Good and Bad of AI Tools in Novice Programming Education

As AI coding tools become more prevalent in programming, it is essential to understand how they influence programming education. This study, conducted in a first-semester Introduction to Programming course, aimed to determine the positive and negative effects of these tools on students’ learning experiences and their ability to develop essential programming skills. Using a mixed-methods approach, we collected data from 73 teams of engineering students over a 12-week period. Students completed surveys and reported on their AI tool usage. We analyzed this data quantitatively to identify trends in tool familiarity, usage, and student satisfaction. Additionally, qualitative analysis of student reports provided insights into the specific ways AI tools were used and their perceived benefits and drawbacks. The findings revealed a significant increase in AI tool familiarity (from 28% to 100%) and usage among students. Students’ satisfaction with AI tools improved over time. The most prevalent tasks for which novice programmers used AI tools included creating comments (91.7%), identifying and correcting bugs (80.2%), and seeking information (68.5%), while other tasks were less common. While these tools offered benefits like assisting in learning and enhancing real-world relevance, they also raised concerns about cheating, over-reliance on AI tools, and a limited understanding of core programming concepts.

University EFL Students’ Perspectives on Educators’ Digital Competence at Qassim University

Personal and professional success in the digital age requires digital abilities. Increased expectations that graduates be adept in digitisation while joining the job need higher education institutions to successfully incorporate digital aspects. This study evaluates university EFL students' digital integrative attitudes and instructors' digital proficiency. Digital tools are emphasised for collaborative learning. Researchers seek to address two key questions: What are EFL students’ perspectives on digital learning? How do they assess teachers' IT skills? It appears through the end result not all instructos have the same digital competency and it is strictly field dependent. This variance in the digital capalities and field orientation decideis the choice of their preferredd pedagogical tools. Digital collaborative learning was used by both categories of students: engineering students and non-engineering EFL students to variable degrees. This suggests that punishing students may affect preferences and behaviours in this area. EFL students were confident in their digital tool usage, although they didn't comprehend all the features. This study's results may improve instructional techniques, institutional standards, and professional development programmes to improve educators' digital abilities. As digital technology integration evolves, global higher education consequences are enormous.