Exploring the Potential Application of Artificial Intelligence Tools in Preparing For ABET Accreditation

Currently, accreditation represents a means of evaluation of the performance level of university programs, so it has a strategic importance in improving the academic quality of these programs. Therefore, having appropriate mechanisms to elaborate procedures and obtain information for accreditation purposes is necessary. In this paper, a methodology to collect information and measure student outcomes achievements for ABET accreditation purposes is proposed, in order to facilitate the elaboration of the self-study report required by this accreditation process. The development of criteria for collecting information in the courses of the curriculum and obtaining effective measurements of student outcomes, as well as an appropriate sequential flow that facilitates the development of self-study report are described. The proposed methodology was used in the ABET accreditation and re-accreditation process of an Electronic Engineering program.

Objective: To explore the perception of university students on the use of Artificial Intelligence (AI) tools for the development of autonomous learning. Theoretical Framework: The research is based on Technological Acceptance Theory and Constructivism, focusing on the perception of AI in autonomous learning of university students. Method: Quantitative approach with a descriptive scope, the sample consisted of 665 students enrolled in the Faculty of Education Sciences and Languages (FCEI) of the Peninsula de Santa Elena State University (UPSE)-Ecuador; in the collection of information, the Questionnaire of Perception on the Use of Artificial Intelligence for Autonomous Learning was designed based on 4 dimensions of both variables, and the statistical program SPSS version 29 was used for data processing. Results and Discussion: The results indicate that students show a favorable perception towards the use of AI tools for the autonomous learning process, however, although AI is recognized as a potential tool in university environments, there are still challenges to be overcome. Research Implications: The study has practical implications for strengthening in students the digital competencies needed to effectively use AI tools in their autonomous learning. Originality/Value: The research provides data on the perception of AI tools among university students, offering a starting point for future technology integration strategies in universities.

BackgroundUndergraduate program accreditation for undergraduate engineering programs is challenging to faculty. Departments are required to accumulate data and then apply measures to both their outcomes and objectives in their program. The Accreditation Board for Engineering and Technology, ABET, is an accreditation organization for college and university programs in applied science, computing, engineering, and technology. The quality of engineering programs has become a fundamental issue in the educational system in the United States. Accreditation confirms that the institution or the program meets minimum quality criteria. ABET has become more important when licensure is required in many states for engineers to practice. According to information provided in ABET website, “Many state board of professional licensure in engineering and surveying require applicants to have graduated from an ABET‐accredited program. In states where non‐ABET graduates are permitted to be licensed, an additional four to eight years of work experience may be required.” (www.abet.org)ABET provides a sets of goals and objectives for accreditation and evaluates and certifies the engineering related education. In order to prepare for an evaluation of their program, the faculty develops a methodology of assessing the performance with respect to outcomes and objectives that are defined in ABET criteria. Each engineering program develops its objectives and performs internal evaluation to meet ABET criteria.Sources in literature describe different approaches and present various tools to design effective processes for evaluating engineering education and to satisfy ABET criteria. Miller and Olds introduce an evaluation matrix which provides performance criteria, implementation strategies, and evaluation methods. (Olds 1998) Owen, Scales, and Leonard explain the importance of creating a database of outcome indicators for engineering programs. There are many other sources in the literature describing different techniques to create surveys for measuring success and providing strategies to integrate assessment and evaluation into engineering education [Owen 1999].Besides the sources in literature, experiences of institutions when they were accredited provide various approaches. The United States Military Academy (USMA) prepared multiple programs for a joint ABET accreditation. They first established the committees for each department and developed cross‐program reviews. The cross‐program review helped them to identify problem areas and make to correction early. By assigning individual faculty members to be responsible for documenting, the outcome monitoring was established [Huggins 2002].The ECE Department at Cal Poly Pomona developed overall objectives to get accredited: Reviewing department goals and objectives, justifying the program if it meets the goals and objectives, justifying the students if they meet the goals and objectives, assessment and improvement of the program, reviewing course outline, and putting the notebooks together. (http://www.csupomona.edu/~ece/eceweb/abet.html)The College of Engineering at The University of Wisconsin Madison outlined four primary activities: Developing department level outcomes, developing course objectives for undergraduate courses, hosting a working conference on ABET preparation, and developing a web‐site for accreditation and assessment (Pfatteicher 2000).At the University of Arizona, the department of Engineering established two teams: ABET implementation team and ABET assessment team. The implementation team consisted of one person from each department to coordinate the activities and assessment team had individuals with specific skills. They created educational objectives and expected outcomes. Expected outcomes later were expressed in terms of measurable activities such as the outcome of working in a multi‐disciplinary team was expressed in terms of completing team lab exercises and completing Engineering 102 projects. They used primary assessment tools such as student, alumni, and faculty surveys and portfolios (Johnson 1999).As can be seen by the various references above, each engineering department may use different processes to achieve the same results, accreditation.The purpose of this paper is to present the process used by the Systems Engineering and Operations Research Department at George Mason University to accredit our Systems Engineering undergraduate program with special emphasis on measurement of outcomes and objectives.

This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.

This article provides an overview of artificial intelligence (AI) tools that support researchers, postgraduate and doctoral students at different stages of the scientific research process in higher education. The study emphasizes how AI tools, especially special AI bots, enhance research efficiency and strengthen researchers’ capabilities. In this study, the scientific research cycle is outlined step by step, including problem definition, literature review, research design, data collection, data analysis, interpretation, academic writing, and presentation. By mapping specific AI tools to each stage of this cycle, the article demonstrates how AI can streamline workflows and enable more effective and efficient scientific research.

Generative artificial intelligence (AI) tools are increasingly present in academic writing workflows, and their irresponsible use poses a growing threat to the integrity of scholarly publishing. In this editorial, we highlight the emergence of AI-generated references, or so-called “ghost references”, as a specific concern for JCOM and the wider academic community. We consider how AI tools like large language models can produce convincing yet fictitious citations that might bypass standard peer review. Also, we reaffirm JCOM's policy requiring full disclosure of any generative AI use in preparing manuscripts, remind authors that the responsibility for accuracy and integrity lies with those whose names appear on submissions, and outline our commitment to reject, withdraw, or retract manuscripts found to contain fabricated content at any stage of the publication process. As a journal dedicated to science communication, JCOM maintains a strong focus on the honest and transparent development of knowledge.

Higher education institutions (HEIs) play a vital role in preparing the country’s future workforce by equipping students with the knowledge and skills needed to succeed, from enrollment to employment. This study analysed the employment outcomes of Sorsogon State University graduates from Batch 2021, focusing on trends in employment and unemployment rates by academic program, gender, and employment sector. A quantitative descriptive research design was utilized. Data were collected through an online survey of 824 graduates in the Engineering, Technology, Business and Management, Education, Information Technology (IT), and Agriculture and Fisheries programs. The study revealed that the majority of university graduates from Batch 2021 are employed, but the employment outcomes vary across academic programs. The graduates of the Technology, Business and Management, and Engineering programs had higher employment rates, whereas the graduates of the IT, Education, Agriculture, and Fisheries programs had higher unemployment rates. The gender-based trends showed that the male graduates of the Engineering and Technology programs had higher employment rates, whereas the female graduates of the Business and Management, Education, and Agriculture and Fisheries programs had better employment rates. Most graduates are employed in the private sector, with Engineering and Technology graduates leading in private sector jobs. Self-employment is more common among IT, business and management, and education graduates. There is a need to strengthen partnerships with local businesses, government agencies, and international companies to create more job opportunities for programs with lower employment rates, particularly in IT, Education, Agriculture and Fisheries. Future research may be conducted to provide deeper insights into the factors influencing the employment outcomes of graduates.

Historical buildings play a pivotal role in enhancing architectural identity through their stylistic characteristics. They offer a unique set of architectural elements that enhance the value of the city and give it a distinctive character. With technological developments, artificial intelligence has emerged as an effective tool for dealing with historical buildings in general. However, the influence of AI tools on the Integrity of architectural model levels at which stylistic characteristics are manifested has not yet been proven. Hence the research problem appeared as (lack of clarity regarding the impact of artificial intelligence technologies on architectural model levels reflecting stylistic characteristics and ensuring the integrity of historical buildings). From here, the research goal took shape as (determining the impact of artificial intelligence technologies and tools on the Integrity of levels reflecting the stylistic characteristics of historical buildings). The importance of this research lies in providing a framework for the capabilities that artificial intelligence provides in historical buildings, at the level of documentation and development. It has been found that artificial intelligence tools such as ideogram are most effective in ensuring the integrity of The Associated stylistic characteristics (color/proportions/decorative features/material). They enhance the integrity of stylistic characteristics at their formal levels by being able to accurately monitor and diagnose structural problems of the building, which allows taking the necessary preventive measures. AI tools at the formal level have proven to be more effective than at the functional and aesthetic levels. The study recommends careful selection of AI tools to ensure their Integrity at the level of documentation and development.

With parallel and distributed computing (PDC) now wide-spread, modern computing programs must incorporate PDC within the curriculum. ACM and IEEE Computer Society's Computer Science curricular guidelines have recommended exposure to PDC concepts since 2013. More recently, a variety of initiatives have made PDC curricular content, lectures, and labs freely available for undergraduate computer science programs. Despite these efforts, progress in ensuring computer science students graduate with sufficient PDC exposure has been uneven.This paper discusses the impact of ABET's revised criteria that have required exposure to PDC to achieve accreditation for computer science programs since 2018. The authors reviewed 20 top ABET-accredited computer science programs and analyzed how they covered the required PDC components in their curricula. Using their own institutions as case studies, the authors examine in detail how three different ABET-accredited computer science programs covered PDC using different approaches, yet meeting the PDC requirements of these ABET criteria. The paper also shows how ACM/IEEE Computer Society curricular guidelines for computer engineering and software engineering programs, along with ABET accreditation criteria, can cover PDC.

Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks.

Even after multiple cycles of ABET accreditation, many engineering programs are unsure of how much curriculum content is needed to meet the requirements of ABET's Criterion 3.f (an understanding of professional and ethical responsibility). This study represents the first scholarly attempt to assess the impact of curriculum reform following the introduction of ABET Criterion 3.f. This study sought to determine how much professional and ethical responsibility curriculum content was used between 1995 and 2005, as well as how, when, why, and to what effect changes in the amount of content occurred. Subsequently, the study sought to evaluate if different amounts of curriculum content generated differing student outcomes. The amount of curriculum content used by each of the participating programs was identified during semi-structured interviews with program administrators and a review of ABET Self-Study documents. Quantitative methods were applied to determine if a relationship existed between the curriculum content and performance on a nationally administered, engineering-specific standardized examination. The findings indicate a statistical relationship, but a lack of structure between the amount of required content in the curriculum and performance on the examination. Additional findings were also generated regarding the way that programs interpret the Criterion 3.f feedback generated during accreditation visits. The primary impact of this study is that it dispels the myth that more courses or course time on professionalism and ethics will necessarily lead to positive engineering education outcomes. Much of the impetus to add more curriculum content results from a lack of conclusive feedback during ABET accreditation visits.

Accreditation was originally conceived and applied as an assessment tool for the quality of university programs. However, its emphasis shifted to program objectives and learning outcomes from the original prescriptive focus on measurable inputs. Accreditation normally confirms that the institution/department offering a program has established suitable processes and resources to ensure that the required quality and standards are achieved. Through the years, it has become the fundamental quality assurance mechanism for engineering education (Prados et al. 2005; Sarin 2000; Woollacott 2009). Accreditation is an important step in the life of any professional program, especially for international benchmarking. In the case of engineering programs, accreditation by any signatory of the Washington Accord guarantees international recognition, a feature sought after by many colleges of engineering, especially those in developing countries. Accreditation confirms that graduates have attained a recognized academic standing and that they possess the professional competence to practice engineering. In a majority of developed countries, graduation from an accredited program is the primary step toward professional registration; however, in most developing countries, graduation from any engineering program confers sufficient license to practice engineering. In these countries, graduation from an internationally accredited program provides additional recognition and acceptance. The international recognition of an engineering program has become important in recent years and is likely to be promoted by the increasing mobility of engineering graduates. It is particularly important for graduates in developing countries, who aspire either to work in more developed countries or in multinational companies. Graduation from an accredited program assures possible employers of the quality of education of their entry-level professionals. Because employability of graduates is one of the primary indicators of success and ranking of engineering programs, the competition for talented students has inevitably created a strong tendency, almost a necessity, for engineering programs to be internationally accredited. The accreditation requirements of different accreditation bodies may differ in form and detail, but all expect a considerable amount of documentation as the basis for evaluation. The preparation of the documents can be extremely challenging for first-time program presenters; preparation for each revalidation of the accreditation is also a time consuming activity. This paper advocates that, although the accreditation process is very demanding, it provides a great opportunity for a comprehensive review of an academic program. Ideally, the accreditation process should not only improve the program itself, but should also promote the development of the faculty that delivers the program. However, neither of these two objectives is usually achieved in developing countries. The following argument is based on the authors’ extensive experiences in the management and delivery of engineering programs in different institutions and on their involvement in the preparation of accreditation documents. Unfortunately, the accreditation of a program is not, in itself, a guarantee that the spirit intended by accreditation bodies is captured and implemented in the program.

Technology today has a very significant influence on all sectors of life. This is marked by the Industrial Revolution 4.0. In the world of education, this is certainly a matter of great concern. Technological developments that occur can be a tool for teachers and students to carry out learning and teaching activities. The technology in question is the presence of Artificial Intelligence (AI). Currently, AI is experiencing an increase in its use. Students in various levels of education are active users of technology, so AI has enormous potential to play a significant role now and in the future. This study aims to determine the intention to use AI tools among students from various educational levels, such as elementary, junior high, high school, undergraduate, graduate, and doctoral. The research method used in this research is quantitative method, where this research distributes questionnaires using Microsoft Form with respondents who are students from various levels of education and have used AI tools in the learning process. The model used in this study uses the Technology Acceptance Model (TAM) model. This study shows that the Trust variable has no effect on Attitude Toward Using. This can be seen from the t-test value of less than 1.97, namely 1.623 and a p-value of more than 0.05, namely 0.105. Future research is recommended to be able to add other variables, use other research models, and be able to classify the level of education.

BackgroundOne way to improve quality and safety in healthcare organizations (HCOs) is through accreditation. Accreditation is a rigorous external evaluation process that comprises self-assessment against a given set of standards, an on-site survey followed by a report with or without recommendations, and the award or refusal of accreditation status. This study evaluates how the accreditation process helps introduce organizational changes that enhance the quality and safety of care.MethodsWe used an embedded multiple case study design to explore organizational characteristics and identify changes linked to the accreditation process. We employed a theoretical framework to analyze various elements and for each case, we interviewed top managers, conducted focus groups with staff directly involved in the accreditation process, and analyzed self-assessment reports, accreditation reports and other case-related documents.ResultsThe context in which accreditation took place, including the organizational context, influenced the type of change dynamics that occurred in HCOs. Furthermore, while accreditation itself was not necessarily the element that initiated change, the accreditation process was a highly effective tool for (i) accelerating integration and stimulating a spirit of cooperation in newly merged HCOs; (ii) helping to introduce continuous quality improvement programs to newly accredited or not-yet-accredited organizations; (iii) creating new leadership for quality improvement initiatives; (iv) increasing social capital by giving staff the opportunity to develop relationships; and (v) fostering links between HCOs and other stakeholders. The study also found that HCOs' motivation to introduce accreditation-related changes dwindled over time.ConclusionsWe conclude that the accreditation process is an effective leitmotiv for the introduction of change but is nonetheless subject to a learning cycle and a learning curve. Institutions invest greatly to conform to the first accreditation visit and reap the greatest benefits in the next three accreditation cycles (3 to 10 years after initial accreditation). After 10 years, however, institutions begin to find accreditation less challenging. To maximize the benefits of the accreditation process, HCOs and accrediting bodies must seek ways to take full advantage of each stage of the accreditation process over time.