Artificial intelligence (AI) is revolutionizing the way programming is taught and learnt and is redefining t he way programming skills are assessed in the continuously changing field of computer science education. This paper introduces a novel approach for teaching and evaluating programming knowledge by reversing Bloom's taxonomy in order to accommodate AI-powered learning. Traditionally, Bloom's taxonomy progresses from basic cognitive skills like remembering and understanding to advanced skills like creating. With AI tools such as WebSim.ai and Anthropic Artifacts now capable of generating sophisticated outputs, the focus shifts away from students' ability to create, as these tools handle that task effectively. Instead, this paper proposes placing a higher emphasis on students' ability to understand and critically analyze AI -generated solutions, assessing their comprehension and ability to reverse-engineer existing work. We call this the Reverse Bloom Taxonomy, where students begin with creation and then move toward deeper understanding of the subject matter. This paper outlines the methodology for applying this reversed framework in programming education and presents a promising concept for improving student learning outcomes. The discussion addresses challenges in implementation and emphasizes the strategic integration of AI -driven learning to prepare students for a technology-driven workforce.
Access to education is a fundamental human right; however, girls and women in Afghanistan face numerous restrictions and barriers that the Taliban regime has imposed. Despite these issues, employing technologies, particularly Artificial Intelligence (AI), provides convenient solutions to facilitate education and empower Afghan girls and women. In this paper, we explore how online educational platforms can assist in providing quality education to Afghan girls and women. It analyzes current digital delivery approaches with incorporated AI technology, including generative AI, Intelligent Tutoring System (ITS), chatbots, and SMS-based learning platforms, that are accessible on low-cost devices and do not rely heavily on high-speed internet connections, and outlines suitable pathways to overcome the existing restrictions. By employing AI technology, a personalized learning environment can be created to deliver quality education based on the individual needs of Afghan girls and women. A survey is conducted to explore the feasibility of implementing an online educational platform in Afghanistan, considering challenges such as internet connectivity, availability of electricity, access to digital devices, and time constraints. Based on successfully implemented programs in another region, we listed recommendations for the policymakers, None governmental organizations (NGOs), and stakeholders to introduce customized Online educational platforms for Afghan girls and women.
In today's fast-paced world, teachers seek continuous improvement for their courses to match their context using e-learning systems. With the aid of technologies, teachers have access to vast amount of resources which requires guidance in selecting appropriate pedagogical resources according to their context. Pedagogical resources recommender system should take into account this context to offer best recommendations dedicated to this user-teacher's context. This paper explores the impact of context-awareness in enhancing the performance of pedagogical resources recommender systems, focusing on three main approaches: contextual pre-filtering, contextual post-filtering, and contextual modeling. This paper proposes an enhancement to the context-awareness of a 2D pedagogical resources recommender system by integrating the contextual modeling approach into a collaborative-filtering recommendation technique. The evaluation of the proposed approach showed a significant improvement of recommendation accuracy compared to the contextual pre-filtering and post-filtering approaches.
Intelligent learning environments integrate various functions for processing and analyzing learning data by leveraging learning analytics theory. Based on this data, numerous machine learning (ML) and artificial intelligence (AI) models are created to predict learning behavior and learning performance with main purpose to improve learning, assessment and teaching. The important role of self-assessment conducted by students in support of their learning progress and final results continues to be explored and discussed. Despite the availability of various educational software products, there is a need to develop intelligent applications to enhance students' self-assessment. The purpose of the paper is to present a conceptual framework and its subsequent implementation by developing a set with web applications based on ML and AI techniques. The applications are related to self-assessment of affective state, predicting and planning learning tasks, self-assessment of graphic objects, knowledge gaining from conversational assistant and self-assessment of learning performance.
This systematic literature review examines the state of LGBTQIA+ inclusion in software engineering education. The review synthesizes findings from various studies t o highlight the experiences and perceptions of LGBTQIA+ students and educators, as well as the impact of inclusive pedagogical practices. Results indicate a predominantly heteronormative and exclusionary climate negatively affecting LGBTQIA+ students' sense of belonging and retention. Innovative pedagogical approaches, such as integrating LGBTQIA+-inclusive problem sets and readings on diversity, show promise in fostering more inclusive environments. The review underscores the need for systemic changes, including increased visibility of LGBTQIA+ issues, diversity training, and supportive networks, to create a more inclusive and equitable educational landscape. Future research should focus on expanding these efforts and exploring the perspectives of LGBTQIA+ educators to fully understand and address the challenges they face within software engineering higher education.
The digital transformation of the educational domain is an ongoing process that covers all study areas and all degree levels - from the primary school to the university. This process is constantly fuelled by the development of newer information and communication technologies, collaboration tools and hardware platforms and by their integration in the different courses and educational processes. In this paper we provide a review on the most popular current technologies for digital transformation of the education, followed by an analysis on the emerging solutions and tools for the digitalization of the teaching and learning processes. Next, the paper presents a discussion on the challenges and issues of the digital education. Besides the direct use of the technologies for digital transformation of the education, we also provide a different point of view on the subject by analysing the needs, the advantages and the disadvantages from the introduction of the actual technologies as learning subjects in the higher education.
This study explored the attitudes of educators in Saudi universities towards using virtual labs in programming courses. A descriptive analytical approach was used to analyse questionnaires to obtain data. 50 educators participated from five Saudi universities: the Saudi Electronic University, King Saudi University, Prince Sultan University, Princess Nourah Bint Abdul Rahman University, and King Khalid University. An online questionnaire form was distributed to them via two official academic WhatsApp groups. The results show that the attitudes of the educators in Saudi universities towards using virtual labs in programming courses are positive. According to the educators, using virtual labs in programming courses improves students critical thinking, problem-solving, and creative thinking skills. It improves students' searching, observation, and exploration skills. This paper recommends adding virtual lab-related activities to the curricula of programming courses in Saudi universities.
This paper introduces a European project aimed at better preparation of students preparing for graduation from technical universities for their transition from academia into a real work environment. Simultaneously, the students should be supported in acquiring new skills and abilities that are typically not included in the curricula of technical universities, but will be desirable or even necessary in their academic, professional, and personal lives later. We are focusing on two aspects of this problem. The first one involves identification of missing topics in the curricula at technical universities, including wider areas of mental health, sustainability, and multiculturalism. The second one describes a new approach to education based on pure e-learning (not blended learning) and relying on the innovative principles of next-generation educational materials that are becoming known as x-learning.
In the rapidly evolving field of education, emerging technologies are increasingly reshaping how learning takes place. Virtual reality (VR) allows users to fully immerse themselves in simulated worlds, giving them the impression that they are in a completely different environment. This article examines how immersive, interactive virtual reality environments are narrowing the gap between theoretical knowledge and practical application, impacting traditional teaching methods and how using modern technology can improve the quality of education even more. The study also highlights the CareProfSys research project, which demonstrates how VR can transform education and better prepare students for today's jobs by combining VR, AI, and data analysis to develop personalized career counselling.
Data is the foundation of all machine learning applications. In education science, especially for the learner characteristics that drive personalized learning, it is difficult to collect and often uncertain. It is challenging to model, train, evaluate, and analyze the underlying algorithms when developing AI-based systems and having small sample sizes. To address these problems, we present a synthetic data generator utilising probabilistic models. This generator can effectively model and simulate complex learner profiles. To achieve this, we collected extensive data on learning styles, learning strategies, personalities, and preferred learning paths from 593 students over several semesters at a higher education level. Then, Bayesian networks, Hidden Markov Models, and Markov Chains are used to model the relationships between learner profiles. Using the Bayesian information criterion, and cross-validation with log-likelihood scores, we compare various models to select the best fitting one for synthesizing the data. The synthetic data is then evaluated using statistical validation techniques. In addition, we developed a simulation module with the option to simulate learner profiles based on manual user-defined inputs. The data and code used in this work are available as open source 1contributing to open science and developers for customized simulated data. In the future, this data will refine the training, evaluation, analysis, and benchmarking of algorithms for personalized learning.
Learning and optimizing complex skills in disciplines such as golf is mainly based on the work of human trainers. Technology-based options such as video analysis and motion capturing are already used in some cases to review performance retrospectively. However, technological possibilities offered by the digital age, such as promising technologies like Extended Reality or tactile systems, have not yet been fully exploited. Methodical concepts for applying such systems in training are lacking. This article presents a concept of golf pitch training that uses modern technological possibilities to provide visual and tactile information in real-time. Specifically, this includes sensor-based wrist motion capture to establish a motion model (targeted value) and record the learner's wrist motion (actual value) for later comparison. Extended Reality is applied to visualize augmented information on optimal wrist motion. Vibration modules coupled with accessory hardware provide tactile details.
The escalation in capabilities of Large Language Models has triggered urgent discussions about their implications for tertiary education, particularly regarding how they might facilitate academic misconduct in graded engineering coursework. However, graduate research education — where a student works closely with a supervisor over years to develop both implicit and explicit research skills — has received comparatively less attention in this discussion. This paper seeks to develop this discourse by presenting targeted case studies that explore the opportunities and threats posed by artificial intelligence to engineering doctoral education. For instance, using a specimen exercise from a PhD-level research skills module, we demonstrate how artificial intelligence tools can now deeply penetrate research workflows in technical computing and scripting. We likewise investigate the capabilities of chatbot tools to assist engineering PhD candidates with the broader research skills central to their training and development. These include writing and proofreading theses and research papers, producing data visualizations, simulating peer review processes, and preparing scientific diagrams. By evaluating the capabilities and limitations of extant artificial intelligence in these areas, we can discuss both the potential benefits and ethical concerns of doctoral students engaging with such assistance.
Powerful chatbots, based on intensively-trained large language models, have recently become available for consumer use. The ability of such chatbots to provide credible textual responses to sophisticated engineering problems has been demonstrated in various subfields. This paper seeks to gauge the extent to which such a chatbot can be prompted to complete a set of homework and project exercises for university-level courses in analog, digital, mixed -signal, and signal processing classes. The purpose of this paper is to delineate and clearly articulate the present capabilities of artificial intelligence tools to complete coursework taks across the field of circuit theory. Building on these research findings, this paper suggests practical ways to mitigate artifical intelligence chatbot tools' disription to academic integrity and genuine learning in universities.
Educational robotics is becoming an increasingly important part of the teaching process in schools. When integrating it into education, it is essential to clearly define educational goals and answer the question of what we want to teach students. At the same time, it is crucial to find ways to effectively measure the extent to which these goals are being met. Student assessment is an integral part of this process, but the question arises as to which assessment methods are appropriate for use in educational robotics. This study examines the impact of self-assessment as a method of student assessment when working with educational robotics. The research focuses on three key areas: robotic model construction, programming, and teamwork. Students regularly assessed their performance through prepared rubrics and compared them to teacher assessments, which supported their self-reflection and the development of critical thinking. The results showed that the quality of the self-assessment and the agreement between the self-assessment and the teacher's assessment differed depending on the complexity of the task, with the best results being achieved in the programming field. The study also highlights the importance of teacher feedback in overcoming challenges associated with student self-assessment. The findings of this study provide valuable insights for teachers who want to implement self-assessment through rubrics into the teaching process in educational robotics.
The current global economic challenges are placing significant demands on employers in the engineering sector to seek industry-ready competent engineers who can quickly contribute without extensive training. However, employers often face the dilemma of selecting candidates from a pool of recent graduates who may lack the hands-on skills needed to navigate traditional manufacturing methods, troubleshoot issues, and improve current processes using modern technological advancements. In today's competitive economic landscape, academic excellence alone does not guarantee successful employment. Employers now value generic skills like creativity, self-confidence, innovation, and hands-on problem-solving abilities more than ever. The skills gap highlighted by industry in recent engineering graduates working in practical settings underscores the need for enhancements in the current higher education system to better align with industry demands. Research in the education sector has explored the potential of technology, particularly Mixed Reality (MR), to enhance learning outcomes. Recent studies have demonstrated MR's effectiveness in improving college students' overall learning outcomes. While some studies have examined extending MR technology to university students, this area is still in its early stages. This paper presents research conducted at the University of Malta, employing multiple methods approaches to identify and analyse the barriers to knowledge transfer in university-level engineering education. It also introduces a framework for developing a Mixed Reality educational tool aimed at better preparing graduates for the workplace. The paper introduces the initial research work and potential of MR in enhancing training precision and efficiency, with initial insights suggesting significant potential improvements in practical skill acquisition. Through this work, we seek to continue to bridge the gap between academic preparation and industry expectations, positioning MR as a key technology in the evolution of engineering education.
This study provides a succinct overview of the research conducted on the impact of ChatGPT in education. A bibliometric analysis reveals a significant increase in research activity between November 2022 and April 2024. The study identified and analysed the most relevant papers from this period, focusing on 745 relevant studies sourced from the Scopus database. Key findings include the model's contribution to enhancing learner engagement and optimising knowledge acquisition through personalised learning. The study also examines the influence of ChatGPT on curriculum design, assessment strategies, and the emergence of AI-powered educational tools, such as intelligent tutoring systems and chatbots. While highlighting the transformative potential of ChatGPT in education, ethical considerations are acknowledged, including concerns about assessment value reduction, data privacy, and algorithmic bias. Responsible AI integration is emphasised for a balanced and ethical use in learning environments. The bibliometric analysis identifies prominent authors, countries, and subject areas contributing to the field. Kleebayoon, emerges as a prolific contributor, and the United States leads in citations. Co-word and co-citation analyses reveal clusters of keywords and authors, illustrating interconnected themes and relationships in the literature. The study underscores ChatGPT's transformative potential in education, quantifies research trends through bibliometric analysis, and emphasises the importance of responsible AI integration and addressing ethical considerations in the evolving educational landscape.
During the period of confinement caused by the COVID 19 epidemic, universities had to radically transform the way they delivered courses and organized examinations. Many of them opted to go entirely online for both teaching and assessment. Even the most reluctant teachers have been forced overnight to adapt their knowledge assessment and examination practices to online methods. This transition took place as a matter of urgency, and the move to online examinations often consisted of reusing traditional methods and adapting them in an ad hoc and empirical manner to the distance communication tools available. After this period, it seems interesting to see how practices have evolved once everyone has been systematically exposed to online and computer-based examinations. Have we returned to the traditional examination methods of the pre-Covid era in a form of epidermal reaction to reject this period? Have we moved on to a massive and voluntary adoption of online modalities? Or are we witnessing another phenomenon that is part of the more general post-digital movement in education that we are currently observing? Well before the COVID epidemic, the University of Geneva had set up an infrastructure and support service enabling teachers to take their exams online on a voluntary basis. This infrastructure was rapidly reinforced to ensure the organization of all distance examinations at the time of the COVID epidemic. In this study, we propose to analyze the evolution of the infrastructure supporting online examinations, before, during and after the COVID epidemic. We also propose to analyze the evolution of examination modalities during these three periods to better understand how the institution adapted and evolved, but also how teachers modified their assessment practices during these periods. We will conclude by discussing the impact that the arrival of AI tools, and in particular generative AI, may have on the use of online and digital examinations.
This study explores the relationship between students' formative self-assessment and psychological capital within a Project-Based Learning (PBL) environment utilizing the digital tool Padlet. The research was conducted with university students across 27 classes, focusing on how psychological capital—comprising self-efficacy, hope, resilience, and optimism—affects students' self-assessment accuracy and engagement in PBL activities. The study found that higher levels of psychological capital significantly enhance students' participation in self-assessment and overall learning outcomes. Specifically, self-efficacy and hope were identified as key predictors of successful formative self-assessment. The findings suggest that integrating digital tools like Padlet in PBL can support more effective self-assessment practices, and that educators should consider strategies to develop students' psychological capital to maximize learning effectiveness.
The modern fit-for-purpose approach to education is STEAM education, which offers students the opportunity to study through problem-solving techniques so they can acquire new knowledge through integrated learning. This helps the students have a greater degree of multidisciplinary knowledge while solving practical scenarios that are offered as problems to the students. On the other side, being immersive and imaginative through Virtual Reality (VR) and Augmented Reality(AR) is becoming a new trend and many educational institutions have embraced the idea of using these technologies interactively within the classes. These technologies are being integrated into teachers planning to offer more practical learning and meanwhile raise student motivation, foster greater student interaction and cooperation, and most important part, through them overcome the obstacle of lacking suitable labs. The purpose of this research paper is to determine whether or not using VR and AR in STEAM classes impacts students' motivation. The research is situated in lower high school, respectively in Math and Chemistry classes where the students are asked to provide written opinions in the first week and last week of the experiment for using both technologies in their classes. The sentiment analysis of these opinions is conducted through the use of a deep-learning multilingual BERT model, named RoBERTa. From a purposive sample of 29 students, the sentiment analysis of using Virtual Reality technologies from the very beginning to the end fluctuated between 69 and 50% for positive, 0.25 and 0.10% for neutral, and between 6% and 40% for negative. The same case was not emphasized for Augmented Reality technology. In conclusion, the Faculty of Education educational programs will be recommended to consider these opinions, especially for the STEAM and ICT programs.
This paper presents the developments of the Erasmus+ project Ethical Engineer: Integrating teaching ethics in artificial intelligence and robotics into Engineering Education. The Ethical Engineer project goal is to enhance AI education by promoting truthful AI in Europe, with authentic ethical, social, and legal aspects in engineering education and training. We focus on engineering and robotics students on university level. In general, ethics in engineering education today is multifaceted, reflecting the complexity and global impact of the engineering profession. By integrating ethics into every aspect of engineering education, from classroom learning to professional development, engineering programs will graduate not just skilled engineers, but responsible, ethical professionals committed to the greater society. The project brings together a diverse mix of participating organizations that complement each other in terms of expertise, experience, and perspectives. The consortium consists of universities with a strong academic foundation with advanced knowledge in AI, robotics, and data science, as well as access to cutting-edge research and innovation. The organization of engineering education facilitate the exchange of best practices, ideas, and resources among their member institutions, broadening the project's results and impact. This collaboration is further enhanced by the practical industry experience and insights on AI ethics by professional company partners. We describe the goals of this project and give a case example of how to include ethics in engineering education.
