Technological Innovation in Engineering Education: A Psychopedagogical Approach for Sustainable Development

Technological Innovation in Engineering Education: A Psychopedagogical Approach for Sustainable Development Item Type info:eu-repo/semantics/article Authors Lourenço, Abílio; Navarro-Loli, Jhonatan S.; Domínguez-Lara, Sergio DOI 10.3390/su17146429 Publisher Multidisciplinary Digital Publishing Institute (MDPI) Journal Sustainability Switzerland Rights info:eu-repo/semantics/openAccess; Attribution 4.0 International Download date 04/11/2025 11:55:24 Item License http://creativecommons.org/licenses/by/4.0/ Link to Item http://hdl.handle.net/10757/686712 Academic Editors: Christos Troussas, Sébastien Jacques, Zoe Kanetaki, Idiano D’Adamo and Constantinos Stergiou Received: 22 May 2025 Revised: 9 July 2025 Accepted: 10 July 2025 Published: 14 July 2025 Citation: Lourenço, A.; Navarro-Loli, J.S.; Domínguez-Lara, S. Technological Innovation in Engineering Education: A Psychopedagogical Approach for Sustainable Development. Sustainability 2025,17, 6429. https:// doi.org/10.3390/su17146429 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Technological Innovation in Engineering Education: A Psychopedagogical Approach for Sustainable Development Abílio Lourenço 1,* , Jhonatan S. Navarro-Loli 2and Sergio Domínguez-Lara 3 1Institute of Education, University of Minho, 4710-057 Braga, Portugal 2 Facultad de Psicología, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru; [email protected] 3Instituto de Investigación Facultatad de Ciencias de la Comunicación, Turismo y Psicología, Universidad de San Martín de Porres, Lima 15036, Peru; [email protected] *Correspondence: [email protected] Abstract Digital transformation has profoundly impacted engineering education, demanding new pedagogical approaches that ensure effective and sustainable learning. Educational psychology plays a fundamental role in strategically integrating educational technologies, fostering more inclusive, interactive, and efficient learning environments. This article explores the intersection of technological innovation, engineering education, and educational psychology, analyzing how digital tools such as Artificial Intelligence, virtual reality, gamification, and remote laboratories can optimize the teaching–learning process. It also examines the psychopedagogical impact of these technologies, addressing challenges like cognitive load, student motivation, digital accessibility, and emotional well-being. Finally, the article presents guidelines for sustainable implementation aligned with the Sustainable Development Goals (SDGs), promoting efficient, equitable, and student-centered education. As a theoretical and exploratory study, it also points to directions for future empirical investigations and practical applications. The insights provided offer strategic guidance for academic managers and educational policymakers seeking to implement sustainable, inclusive, and pedagogically effective digital innovation in engineering education. Keywords: technological innovation; engineering education; psychopedagogical strategies; digital learning; sustainable development goals (SDGs) 1. Introduction Technological innovation has been a transformative force in engineering education, especially with the rise of Industry 4.0, which introduced new technologies such as Artificial Intelligence (AI), augmented reality (AR), big data, and the Internet of Things (IoT). These advancements require higher education to adapt in order to prepare students for the challenges and opportunities in a highly digital and dynamic environment [ 1 ]. In recent decades, engineering education has incorporated these technologies to enhance the effectiveness of traditional methodologies, which often focused on theoretical classes and limited practical sessions [ 2 ], and to develop competencies that enable addressing the issues of the current century and fulfilling the Sustainable Development Goals [3]. In particular, digital tools such as virtual simulations, online learning platforms, and remote laboratories have been utilized to facilitate learning and create more interactive and accessible environments, where students can experience real engineering situations safely and in a personalized manner [ 4 ]. This transformation improves the understanding Sustainability 2025,17, 6429 https://doi.org/10.3390/su17146429 Sustainability 2025,17, 6429 2 of 21 of complex concepts and allows for more flexible and adaptive learning, adjusting to the needs of each student, thus favoring the achievement of individual objectives. Technological innovation in engineering education is not merely a technical matter but also a psychological and pedagogical one. Psychopedagogy not only supports the integration of technology in education but also plays a crucial role in ensuring that such innovation is sustainable and inclusive over time. By promoting personalized learning, emotional regulation, and metacognitive strategies, psychopedagogical approaches help reduce dropout rates and enhance student engagement, particularly in digital environments [ 5 ]. These strategies ensure that technological adoption is not superficial or short-lived but rather embedded in pedagogical practices that adapt to diverse cognitive, social, and emotional needs. Furthermore, psychopedagogical models advocate for the thoughtful use of resources and tools that foster autonomy, collaboration, and digital well-being, aligning with the sustainability principles outlined in SDG 4 (Quality education) and SDG 9 (Industry, innovation, and infrastructure) [ 6 ]. For instance, immediate feedback systems, learning analytics, and adaptive digital environments contribute to more efficient learning processes and reduce the need for intensive physical infrastructure, thus minimizing the environmental impact [ 7 ]. In this sense, psychopedagogy is not merely a complementary element in digital transformation, it is a key enabler of educational innovation that is both equitable and environmentally responsible. The introduction of new technologies must be accompanied by an understanding of the cognitive and emotional processes involved in learning [ 8 , 9 ] because they impact the generation of educational changes at both individual and institutional levels [ 10 ] and affect the teaching and learning process, generating debates about their impact on students’ experience, as well as their perceptions of effectiveness, challenges, and opportunities [ 11 ]. Psychopedagogy helps to understand how students interact with technology and how learning can be optimized through approaches that promote autonomy and inclusion. Furthermore, it can contribute to the adaptation of educational technologies to different learning profiles to ensure that all students can benefit from the use of technologies, regardless of their cognitive or emotional characteristics [ 5 , 12 ]. This includes practices such as immediate feedback, personalized learning, and the creation of a digital environment that fosters collaboration and the resolution of complex problems. The use of technologies in engineering education is linked to the Sustainable Development Goals (SDGs), in particular SDG 4: Quality education and SDG 9: Industry, innovation and infrastructure [ 13 ], because the university is responsible for supporting and incorporating these principles throughout the entire educational curriculum [ 7 ]. Consequently, when engineering education is combined with innovative technologies, it can offer more accessible, inclusive, and high-quality education that meets the needs of a constantly changing society, contributing to educational sustainability by providing new forms of teaching that do not rely on large physical infrastructures and therefore reduce the environmental impact [ 14 ]. For this reason, the incorporation of sustainable technologies in engineering education not only prepares students for future environmental challenges but also promotes educational practices that respect the principles of equity and accessibility [ 15 ]. Despite the growing volume of studies on educational technologies and engineering education, there is still a lack of integrated approaches that simultaneously consider psychopedagogical aspects—such as cognitive load, motivation, and emotional well-being—and commitments to educational sustainability, in line with the Sustainable Development Goals (SDGs). This paper seeks to fill that gap by offering a transdisciplinary perspective that critically and systematically links these three domains. The originality of this article lies in the proposition of a conceptual framework that, although not yet empirically validated, provides Sustainability 2025,17, 6429 3 of 21 a foundation for future investigations and pedagogical practices that are more inclusive, effective, and sustainable in engineering education. Accordingly, this study is guided by the following research questions: (1) How can psychopedagogical principles inform the effective integration of digital technologies in engineering education? (2) In what ways can the adoption of educational technologies contribute to the sustainability goals, particularly in terms of inclusion, equity, and environmental impact? (3) What conceptual framework can be proposed to guide future empirical studies at the intersection of technological innovation, psychopedagogy, and sustainable engineering education? This study assumes a theoretical and exploratory nature, aiming to integrate perspectives from psychopedagogy and digital technologies applied to engineering education, within a critical and conceptual approach. The analysis is based on the recent scientific literature and intends to identify interrelations and trends that contribute to future empirical research. 2. Technology and Psychopedagogy in Engineering Education Digital transformation has significantly impacted higher education, especially in the field of engineering, where new technologies are redefining the way knowledge is transmitted and assimilated. In this scenario, understanding how pedagogical methodologies have evolved, which psychopedagogical principles support the use of technology in learning, and what the benefits and challenges of this change are becomes essential for more effective education aligned with contemporary demands. 2.1. Evolution of Engineering Teaching Methodologies in the Digital Age Engineering education has traditionally emphasized expository methodologies and approaches based on solving mathematical and physical problems [ 4 ]. However, with technological advancements and the demand for sustainable development competencies such as communication, problem-solving, and professional knowledge, new teaching models have emerged that promote active and personalized learning [16]. In recent years, methodologies such as project-based learning [ 17 ], flipped classroom [ 18 , 19 ], and simulation-based learning [ 20 , 21 ] have been adopted to provide more interactive teaching. The integration of virtual learning environments and tools like virtual and augmented reality [ 22 , 23 ] allows students to experience complex concepts practically, bridging the gap between theory and real-world application. Additionally, digital platforms such as Massive Open Online Courses (MOOCs) and adaptive learning with AI make knowledge more accessible and teaching more flexible [11,24]. While digital technologies have undoubtedly enhanced engineering education—through increased interactivity, personalization, and remote access—it is equally important to acknowledge their potential adverse effects. Critical reflections, such as those by Soulikias et al. (2021) [ 25 ], underscore possible drawbacks of excessive digitalization, including the diminishing of analog and manual skills, a growing dependence on software tools, and the erosion of project-based, intuitive reasoning. These concerns highlight the importance of integrating digital technologies in a balanced and reflective manner, underpinned by structured psychopedagogical approaches that preserve core engineering competencies. 2.2. Psychopedagogical Principles Applied to Educational Technology The introduction of technology in engineering teaching must be guided by solid psychopedagogical principles. This guarantees meaningful and inclusive learning, and one of the fundamental concepts is active learning, which can transform technical content Sustainability 2025,17, 6429 4 of 21 into more engaging learning experiences [ 26 ], as well as strengthen critical thinking and improve academic performance [ 27 ]. This underlines the need to align the introduction of educational technologies with solid psychopedagogical foundations, ensuring that learning in engineering is meaningful and inclusive. Another essential principle is project-based learning, which encourages students to solve real problems collaboratively, improving their critical skills [ 28 ], student autonomy, the development of project management skills, collaborative skills, and enhances communicative competence [29]. Educational technologies, such as simulations and remote laboratories, have been widely studied, standing out for their potential to provide practical and immersive experiences in higher education [ 30 ], with an emphasis on the flexibility they offer in terms of time, place, and learning pace [31]. An additional fundamental aspect is pedagogical differentiation, which focuses on adapting teaching to the individual needs of students, being especially relevant in inclusive education [ 32 ], offering more effective and accessible teaching, aligned with the goal of optimizing the learning experience. The advancement of educational technologies in engineering teaching requires investment in digital infrastructure and psychopedagogical understanding to ensure that these innovations are used effectively [ 33 ]. The combination of active methodologies, personalized learning, and emerging technologies can positively transform higher education, making it more accessible, inclusive, and sustainable, as well as optimizing the learning experience because it improves student motivation, reduces excessive cognitive load, and increases their autonomy in the educational process [34]. Therefore, it is essential to deepen the exploration of specific technological strategies that can be applied to promote sustainable learning, in line with the principles of the SDGs. The implementation of these strategies not only improves the effectiveness of teaching but also contributes to building a more conscious and responsible educational future. 2.3. Benefits and Challenges of the Digitalization of Higher Education in Engineering The digitalization of higher education has significantly transformed teaching and learning processes, especially in areas such as engineering, where practice and experimentation play a key role [ 35 ]. However, this transition presents challenges that must be considered to ensure effective and inclusive implementation [ 36 ]. Modeling and simulation software, such as MATLAB (programming and numeric computing environment), AutoCAD (autodesk for computer-aided design), SolidWorks (parametric 3D computer-aided design and computer-aided engineering tool), and Simulink (extension of MATLAB for modelling and simulation of dynamic systems using block diagrams), allow students to explore complex concepts without relying solely on physical infrastructure, which reduces costs and increases accessibility [ 37 ]. Additionally, online platforms such as MOOCs and learning management systems offer greater flexibility, enabling students to learn at their own pace and according to their individual needs [38]. Digital connectivity also plays an essential role in academic and professional collaboration, as it facilitates interactions between students, teachers, and institutions worldwide. Collaborative projects, hackathons, and international competitions become more accessible, providing enriching experiences that prepare future engineers for a globalized labor market [ 39 ]. At the same time, the application of AI and big data analytics in teaching allows personalized learning, as well as contributing to the monitoring of academic progress and the continuous improvement in pedagogical methods [ 40 ]. Therefore, this transition demands the development of new skills both from teachers, who need to master digital tools and active methodologies, and from students, who must acquire greater autonomy Sustainability 2025,17, 6429 5 of 21 and discipline to face online learning [ 36 ], since the strong practical component of engineering cannot be completely replaced by virtual environments without compromising the development of essential skills. However, the process of digitalizing engineering teaching requires robust technological infrastructure, and not all institutions have the necessary resources to guarantee highquality equipment, stable internet connection, and access to specialized software for all students [ 33 , 41 ]. This situation can exacerbate inequalities among students, compromising equity in education [ 14 ]. To address this challenge, institutional policies that promote digital inclusion in a broader and more structured manner are necessary, going beyond mere provision of infrastructure and teacher training. Such policies should include financial support measures, the development of digital competences in students, and strategies to ensure continuous and equitable access to digital educational resources—especially in regions with lower connectivity or greater socio-economic vulnerability. As critical studies point out, technology, if poorly implemented, can reinforce existing exclusions rather than reduce them [ 42 ]. Furthermore, evidence from meta-analyses indicates that the positive impact of technologies is closely linked to favorable institutional contexts and policies that align pedagogical innovation with digital equity [43]. Despite these challenges, the success of digitalization will depend on the implementation of balanced strategies that combine technology, pedagogical innovation, and digital inclusion. Only with a structured and sustainable approach will it be possible to guarantee that all students can enjoy the benefits of this new educational model [44]. Even though engineering education is becoming increasingly digitalized and the literature on educational technologies is expanding, a significant gap persists in the structured integration of psychopedagogical principles into these innovation processes. Many technological implementations remain focused primarily on operational efficiency, often overlooking fundamental aspects such as students’ emotional well-being, cognitive load, and intrinsic motivation [ 22 , 33 ]. This study proposes a theoretical and exploratory approach that articulates the intersection between technological innovation, psychopedagogy, and sustainability—an area still underexplored in the specialized literature. By identifying these interrelations, the study aims to contribute a solid conceptual foundation to guide future empirical investigations and the implementation of more integrated, inclusive, and sustainable pedagogical practices in engineering education. Notwithstanding the numerous benefits reported, it is important to acknowledge that the effectiveness of educational technologies is not unanimous in the literature. Critical studies highlight that the adoption of digital technologies may, in some contexts, fail to improve learning outcomes or even exacerbate inequalities [ 42 ]. Furthermore, evidence indicates that the positive impact of technologies heavily depends on the pedagogical context, available infrastructure, and teacher preparedness [ 45 ]. A recent second-order meta-analysis by Sailer et al. (2024) [ 43 ] synthesizing 16 meta-analyses in higher education reported that when digital tools merely substitute traditional instruction, there are no substantial gains in cognitive outcomes, whereas moderate to large effects emerged when they enhanced or redefined learning activities, depending on how the technology was implemented. These findings emphasize that technology’s effectiveness is context-sensitive, bolstering the argument for more research on inclusion, sustainability, and psychopedagogical integration. 3. Technological Strategies for Sustainable Learning The integration of technologies in engineering teaching is crucial for making educational practices more effective and aligned with the Sustainable Development Goals (SDGs). Technologies such as AI, virtual reality, gamification, and remote laboratories personalize Sustainability 2025,17, 6429 6 of 21 learning, create immersive environments, and promote socio-emotional skills, reducing the need for physical infrastructure and the environmental impact. Pedagogical differentiation and innovative methodologies make teaching more accessible and sustainable, modernizing education and meeting current social and environmental demands. 3.1. Artificial Intelligence and Personalization of Teaching: How to Adapt Content to the Pace and Individual Needs of Students Artificial Intelligence (AI) has revolutionized the way we learn, particularly through adaptive learning tools that tailor content to each student’s difficulties, abilities, and pace of learning [ 46 , 47 ]. These technologies provide real-time feedback and assistance, allowing students to progress more efficiently while receiving support aligned with their individual profiles [ 48 ]. This personalized approach increases student engagement and contributes to more effective learning processes. One recent example is the Artificial Intelligence-Enabled Intelligent Assistant (AIEIA), developed by Sajja et al. (2024) [ 49 ]. This system combines AI and natural language processing to facilitate access to information, streamline assessments, and offer individualized learning support. Similarly, intelligent tutoring systems leverage AI algorithms to analyze student performance and adjust instructional strategies accordingly, offering customized feedback and dynamically adapting learning materials [35,50]. The integration of AI into education aligns with key psychopedagogical principles. According to Cognitive Load Theory (Sweller, 2017) [ 51 ], adaptive AI systems contribute to more efficient learning by reducing extraneous cognitive load and delivering content at an appropriate level of complexity. This ensures that learners focus their mental resources on meaningful understanding and schema construction. From the perspective of Self-Determination Theory (Ryan & Deci, 2020) [ 52 ], AI can enhance intrinsic motivation by addressing the three basic psychological needs: •Autonomy, through self-paced learning and personalized content paths; •Competence, by offering immediate, specific, and constructive feedback; • Relatedness, when AI tools are embedded in collaborative platforms that promote interaction and peer learning. In a practical application, Tang and Hare (2023) [ 53 ] examined how intelligent tutoring systems combined with gamification elements in an engineering learning context enhanced student motivation and engagement. The study, implemented via a serious game, demonstrated that personalization supported by AI and the motivational appeal of gamified environments can significantly improve learning outcomes. Möller et al. (2024) [ 54 ] evaluated an AI-powered adaptive platform at the International University of Applied Sciences and reported a 27% average reduction in study time over a three-month period. This finding highlights the practical benefits of psychopedagogical personalization using AI in real academic settings. However, integrating AI into education also raises ethical and practical challenges. Concerns related to data privacy, algorithmic transparency, and the risk of over-dependence on automation must be addressed [ 55 ]. It is essential that AI complements, rather than replaces, the role of educators—who are crucial in fostering critical thinking, empathy, and human connection. Thus, effective integration of AI requires adequate teacher training, institutional readiness, and policies that ensure equitable, transparent, and pedagogically sound use of these technologies. When implemented responsibly, AI has the potential to adapt teaching to the diverse needs of learners, making education more inclusive, efficient, and engaging. Sustainability 2025,17, 6429 7 of 21 3.2. Virtual and Augmented Reality: Impact on Experiential Learning and Knowledge Retention Virtual reality (VR) and augmented reality (AR) have emerged as innovative tools that allow students to engage with realistic three-dimensional environments and simulations of physical phenomena and systems engineering. These technologies help bridge the gap between theory and practice, enhancing experiential learning, facilitating comprehension of complex concepts, and improving knowledge retention [ 22 , 23 ]. Importantly, VR and AR increase student motivation and interactivity, making learning more dynamic and engaging [56]. Additionally, VR and AR can contribute to environmental sustainability, aligning with SDG 13, by reducing the need for physical materials and minimizing the carbon footprint associated with travel or energy-intensive physical laboratories. By enabling remote simulations and immersive experiences, these technologies reduce environmental impacts traditionally linked to engineering education infrastructure. Although further studies are needed to quantify these effects, preliminary analyses suggest significant potential to lower resource consumption and emissions through digital substitution. Recent empirical evidence supports the pedagogical potential of VR. Anjos et al. (2024) [ 57 ] conducted a controlled experiment in a higher education engineering program, comparing traditional instruction with VR-based learning. Students in the VR group demonstrated a 29% improvement in knowledge retention compared to the control group. In addition, a meta-analysis of 72 studies reported consistent moderate gains in practical skills associated with VR use in STEM disciplines. These findings mark a shift from theoretical discussions to demonstrable educational impact, showing how VR can be successfully implemented within institutional contexts. Despite these benefits, challenges remain. High costs, lack of adapted teaching materials, and resistance from educators and students can limit VR/AR adoption [ 58 – 60 ]. Addressing these barriers requires institutional investment in teacher training and research focused on optimizing the psychopedagogical integration of these tools [61]. To synthesize the main tools discussed and broaden the analysis with a psychopedagogical focus, Table 1is presented below, outlining the digital technologies applied in engineering education along with their benefits and challenges. Table 1. Digital technologies in engineering education: applications and psychopedagogical challenges. Technology Applications in Engineering Potential Benefits Psychopedagogical Challenges Integration Strategies Augmented Reality (AR) 3D project visualization Visual learning, engagement Cognitive overload, accessibility Simplified UI, scaffolder tasks, multimodal cues Virtual Reality (VR) Immersive simulations Experiential learning Need for pedagogical mediation Guided progression, feedback loops, social tasks Gamification Assessments and motivation Increase in intrinsic motivation Poor design can cause frustration Clear goals, meaningful rewards, autonomy support Remote Laboratories Distance experiments Flexibility, scalability Lack of social interaction Collaborative tools, synchronous sessions Artificial Intelligence (AI) Personalized teaching Adaptive feedback Algorithmic opacity Transparent algorithms, explainable feedback Source: Developed by the authors based on the reviewed literature. Sustainability 2025,17, 6429 8 of 21 The application of cognitive load management and motivation enhancement principles is crucial for the successful use of VR and AR in engineering education. Designing learning experiences that minimize extraneous cognitive demands while supporting autonomy, competence, and relatedness can optimize student engagement and learning outcomes. However, further empirical studies and practical examples are needed to deepen the understanding of these integrations and guide educators in harnessing the full potential of emerging digital technologies. 3.3. Gamification and Project-Based Learning: Greater Engagement and Development of Socio-Emotional Skills The integration of active methodologies, such as gamification and project-based learning (PBL), has proven to be effective in engineering education, promoting greater engagement and the development of socio-emotional skills in students [ 62 – 64 ], which are essential for the labor market [ 65 , 66 ]. Gamification applies game elements, such as scores, levels, and rewards, to increase student motivation and engagement. In engineering education, this approach has been used to make learning more dynamic and interactive, improving content retention and the understanding of complex concepts [67]. On the other hand, PBL places students at the center of their learning, challenging them to solve real problems through practical projects, facilitating the connection between theory and practice, and preparing students for professional challenges [ 68 ], including applications in engineering [41,69]. The combination of gamification and PBL can enhance the benefits of both methodologies. While gamification increases engagement and motivation through playful elements, PBL provides a practical context for the application of these elements, promoting experiential learning [ 70 , 71 ]. This integrated model can be observed in initiatives that use serious games within engineering projects, in which students face real challenges in a gamified environment, fostering both technical development and socio-emotional skills. In the study by Segundo et al. (2022) [ 72 ], PBL was applied in the development and kinematic modeling of robotic manipulators, integrating disciplines such as computer-aided design, robotics, and microcontrollers, resulting in more meaningful and interdisciplinary learning. The effectiveness of gamification and PBL in engineering education can also be interpreted through the lens of Cognitive Load Theory [ 51 ] and Self-Determination Theory [ 52 ], previously discussed in Section 3.1. In this context, gamified tasks help reduce extraneous load through clear goals and feedback, while PBL fosters germane load by promoting active knowledge construction. Both methodologies can support intrinsic motivation by addressing autonomy, competence, and relatedness through collaborative, meaningful tasks. In summary, the integration of gamification and PBL in engineering education offers a solid approach for the development of technical and socio-emotional skills, aligning with contemporary demands for professional training. Recognizing this importance, educational models have been developed that design experiences based on PBL where it is emphasized that the teacher or instructor must be trained for this purpose [62]. Although gamification in engineering education commonly employs game elements in standard learning environments, its implementation through serious games—digital games explicitly designed for educational purposes—has gained prominence. These games allow students to simulate professional challenges in realistic, often collaborative contexts, enhancing not only technical learning but also ethical and socio-environmental decisionmaking. However, as highlighted by Cucuzzella [ 73 ], the use of computational systems to quantify intangible urban qualities in game environments reveals a critical limitation: games, even when carefully designed, may struggle to capture the full spectrum of qualitative, contextual, and emotional dimensions that influence real-world learning and behavior. This insight invites caution: while gamification and serious games promote engagement Sustainability 2025,17, 6429 15 of 21 particularly through the integration of transformative pedagogies that promote critical thinking, social responsibility, and complex problem-solving [ 90 – 92 ]. This educational approach goes beyond the mere transmission of knowledge, transforming the academic environment into a space where engagement, creativity, and the ability to propose innovative solutions to sustainable development challenges are encouraged and cultivated. In this way, universities not only contribute to technical learning, but also shape critical and proactive agents capable of positively influencing the socio-environmental future. To better illustrate this integrative perspective, Table 3presents a conceptual synthesis of the triangular relationship between technology, psychopedagogy, and sustainability in engineering education. This framework highlights how each pillar contributes to shaping a more inclusive, effective, and environmentally responsible learning environment. Table 3. Conceptual summary of the triangular relationship between technology, psychopedagogy, and sustainability. Pillar Key Elements Contributions to Engineering Education Technology Digital tools (VR, gamification, remote laboratories), personalization, accessibility Expands access, reduces environmental impact, facilitates pedagogical innovation Psychopedagogy Cognitive load management, intrinsic motivation, inclusion, mental health, active methodologies Ensures effective learning, emotional support, inclusion, and engagement Sustainability Reduction in resource consumption, electronic waste management, social inclusion, energy efficiency Promotes environmental responsibility, social equity, and economic viability Source: Developed by the authors based on the reviewed literature. In the technological domain, digital resources such as virtual reality, gamification, and remote laboratories provide new pedagogical opportunities, broaden access to knowledge, and contribute to reducing the environmental impact associated with traditional practices [ 87 ]. However, the success of this implementation depends on adequate psychopedagogical support, which considers the management of cognitive load, the stimulation of intrinsic motivation, and attention to students’ mental health, ensuring that the use of technology supports learning without causing overload or exclusion [82]. Sustainability encompasses both environmental aspects—such as reducing natural resource consumption and managing electronic waste responsibly—and social aspects, by promoting digital inclusion and equitable access to technology [ 6 ]. Energy-efficient infrastructures and institutional policies that promote digital sustainability are essential to ensure the long-term impact of educational innovation. The integration of technology, psychopedagogy, and sustainability creates a holistic educational model that prepares critical, ethical professionals aware of their socioenvironmental responsibilities. Educational policies and teaching practices that recognize this interdependence are key to developing engineering education that is innovative, inclusive, and aligned with the Sustainable Development Goals. This interplay must guide policy, curriculum design, and the use of technology to ensure innovation supports both sound pedagogical approaches and sustainability. Sustainability 2025,17, 6429 16 of 21 While gamification supports motivation, engagement, and learning outcomes (SDG 4.1), collaborative platforms like OpenU address broader challenges such as access, teacher development, and global competence integration (SDG 4.3 and 4.7). 5. Conclusions In summary, this study sought to address the research questions proposed in the Introduction by articulating how psychopedagogical principles can inform the integration of digital technologies in engineering education; how educational technologies can contribute to sustainability goals; and how a conceptual framework can guide future empirical research. The insights presented here aim to provide a foundation for further investigations that explore the intersections between technological innovation, psychopedagogy, and sustainability in a more integrated and inclusive way. This theoretical and exploratory study highlighted the main psychopedagogical challenges in the integration of digital technologies in engineering education, emphasizing crucial aspects such as cognitive load management, students’ intrinsic motivation, digital inclusion, and emotional well-being. In addition, the importance of environmental sustainability in the implementation of these educational technologies was emphasized, aligning with the Sustainable Development Goals. Beyond fostering the acquisition of technical and sustainable knowledge, university courses play a fundamental role in awakening a deep curiosity and critical engagement among the new generation of students regarding environmental and social issues. This broader education not only strengthens sustainable knowledge but also encourages students to explore these topics more thoroughly, promoting a proactive and reflective attitude toward global challenges. Thus, higher education becomes a vital space for cultivating conscious, innovative professionals committed to building a more sustainable future. Although this article does not present empirical results, it proposes a conceptual framework that articulates the relationships between technological innovation, psychopedagogy, and sustainability, offering a reference that may guide future investigations and practical applications in the educational context. Thus, this work contributes to a critical and integrated understanding of the potential and limitations of digital tools in engineering education, pointing to the need for empirical studies that validate and expand the reflections presented here. The relevance of this study is therefore highlighted as a basis for theoretical and practical advances that promote more inclusive, effective, and sustainable learning environments. Therefore, although this theoretical and exploratory study offers a relevant conceptual synthesis of the psychopedagogical and sustainability challenges in integrating digital technologies into engineering education, it is important to acknowledge the limitations associated with the absence of empirical data collection and analysis. The reflections presented here should be understood as theoretical propositions, with guiding potential for future research and practical applications, but which require empirical validation. Additionally, the selection of sources and the perspective adopted reflect specific academic and cultural contexts, which should be taken into account when generalizing the findings to other educational realities. Sustainability 2025,17, 6429 17 of 21 Author Contributions: Conceptualization, A.L., J.S.N.-L. and S.D.-L.; methodology, A.L., J.S.N.-L. and S.D.-L.; software, A.L., J.S.N.-L. and S.D.-L.; validation, A.L., J.S.N.-L. and S.D.-L.; formal analysis, A.L., J.S.N.-L. and S.D.-L.; investigation, A.L., J.S.N.-L. and S.D.-L.; resources, A.L., J.S.N.-L. and S.D.-L.; data curation, A.L., J.S.N.-L. and S.D.-L.; writing—original draft preparation, A.L., J.S.N.-L. and S.D.-L.; writing—review and editing, A.L., J.S.N.-L. and S.D.-L. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data is contained within the article. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Schwab, K. The Fourth Industrial Revolution; Crown Business: New York, NY, USA, 2017. 2. Felder, R.M.; Brent, R. Teaching and Learning STEM: A Practical Guide, 2nd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2024. 3. Beagon, U.; Kövesi, K.; Tabas, B.; Nørgaard, B.; Lehtinen, R.; Bowe, B.; Spliid, C.M. Preparing engineering students for the challenges of the SDGs: What competences are required? Eur. J. Eng. Educ. 2023,48, 1–23. [CrossRef] 4. Valença, A.K.A. Metodologias ativas no ensino de engenharia: Uma revisão bibliométrica. Rev. Produção Online 2023,23, 4982. [CrossRef] 5. Esper, M.V.; Tomei, A.J.; Wendland, J. O papel da psicopedagogia na compreensão/mediação das intoxicações tecnológicas. Construção Psicopedag. 2023,33, 8–15. [CrossRef] 6. UNESCO. Agenda Educativa Digital 2021–2025; UNESCO: Paris, France, 2021. Available online: https://siteal.iiep.unesco.org/pt/ bdnp/289/agenda-educativa-digital-2021-2025 (accessed on 18 June 2025). 7. Mohamed Hashim, M.; Tlemsani, I.; Duncan Matthews, R. A sustainable university: Digital transformation and beyond. Educ. Inf. Technol. 2022,27, 8961–8996. [CrossRef] [PubMed] 8. Castro-Alonso, J.C.; Ayres, P.; Sweller, J. Instructional Visualizations, Cognitive Load Theory, and Visuospatial Processing. In Visuospatial Processing for Education in Health and Natural Sciences; Castro-Alonso, J., Ed.; Springer: Cham, Switzerland, 2019; pp. 111–143. [CrossRef] 9. Mayer, R.E. Multimedia Learning, 3rd ed.; Cambridge University Press: London, UK, 2021. 10. Chen, J.; Du, X.; Jiang, D.; Guerra, A.; Nørgaard, B. A review study with a systematic approach: Pedagogical development for educators in higher engineering education. Eur. J. Eng. Educ. 2023,49, 299–329. [CrossRef] 11. Galarza, F.A.C.; Guzmán, I.L.A.; Pilamunga, M.E.M.; Bustamante, M.Á.V. La influencia de la tecnología en la educación superior. Un estudio desde la percepción de los estudiantes. Una revisión sistemática. Recimundo 2025,9, 143–159. [CrossRef] 12. Mendes, V.A.S.; Oliveira Tenório, H.; de Lucca Jardim Borges, L.; Calixto Carrijo, E.; Marra Alves, C. O uso de tecnologias digitais na aprendizagem ativa na engenharia. SciELO 2024, preprints. [CrossRef] 13. UNESCO. SDG 4—Education 2030: Part II, Education for Sustainable Development Beyond 2019; UNESCO: Paris, France, 2019. Available online: https://unesdoc.unesco.org/ark:/48223/pf0000366797.locale=en (accessed on 17 June 2025). 14. Santos, R.A.; Roberg, V.; Pereira, N.R.; Luciano, S.F.M.; Anais do Seminário de Desenvolvimento, Conhecimento e Tecnologia. Desenvolvimento, Conhecimento e Tecnologia—Volume 2; Faculdade Senac Tubarão: Tubarão, Santa Catarina, Brazil, 2024. Available online: https://artigos.devtec.com.br/index.php/devtec/article/view/25 (accessed on 5 June 2025). 15. Dos Santos, S.R. Aprendizagem Baseada em Projetos na formação de engenheiros: Estudo de caso sobre uma experiência curricular. Educ. Teor. E Prática 2023,34, 67. [CrossRef] 16. Du, X.Y.; Kolmos, A.; Ahmed, M.A.H.; Spliid, C.; Lyngdorf, N.; Ruan, Y.J. Impact of a PBL-based professional learning program in Denmark on the development of the beliefs and practices of Chinese STEM university teachers. Int. J. Eng. Educ. 2020,36, 940–954. 17. Lavado-Anguera, S.; Velasco-Quintana, P.-J.; Terrón-López, M.-J. Project-Based Learning (PBL) as an Experiential Pedagogical Methodology in Engineering Education: A Review of the Literature. Educ. Sci. 2024,14, 617. [CrossRef] 18. Bishop, J.; Verleger, M.A. Testing the Flipped Classroom with Model-Eliciting Activities and Video Lectures in a Mid-Level Undergraduate Engineering Course. In Proceedings of the IEEE Frontiers in Education Conference (FIE), Oklahoma City, OK, USA, 23–26 October 2013. [CrossRef] 19. Bhat, S.; Raju, R.; Bhat, S.; D’Souza, R. Redefining quality in engineering education through the flipped classroom model. Procedia Comput. Sci. 2020,172, 906–914. [CrossRef] Sustainability 2025,17, 6429 18 of 21 20. Campos Fialho, B.; Costa, H.A.; Logsdon, L.; Fabricio, M.M. CAD and BIM Tools in Teaching of Graphic Representation for Engineering. In Proceedings of the XXII Conference of the Iberoamerican Society of Digital Graphics (SIGraDi), São Paulo, Brazil, 7–9 November 2018; Blucher: São Paulo, Brazil, 2018. [CrossRef] 21. Wankat, P.C.; Oreovicz, F.S. Teaching Engineering; Purdue University Press: West Lafayette, IN, USA, 2015. 22. Azevedo, V.; Lira, H.; Moraes, A.; Vasconcelos, B. Uso da realidade aumentada no ensino de projeto de engenharia civil. arq. urb 2023,36, 67–79. [CrossRef] 23. Krewer, V.M. A integração de tecnologias emergentes em ambientes virtuais de aprendizagem na era da educação a distância: Tendências e perspectivas futuras no ensino superior. Rev. Multidiscip. Ciências Gerais Focus 2024,1, 42–54. [CrossRef] 24. St-Hilaire, F.; Vu, D.D.; Frau, A.; Burns, N.; Faraji, F.; Potochny, J.; Kochmar, E. A new era: Intelligent tutoring systems will transform online learning for millions. arXiv 2022, arXiv:2203.03724. 25. Soulikias, A.; Cucuzzella, C.; Nizar, F.; Hazbei, M.; Goubran, S. We gain a lot . . . but what are we losing? A critical reflection on the implications of digital design technologies. Open House Int. 2021,46.3, 444–458. [CrossRef] 26. Aguiar, L.R.; Bonilha, G.C.; Mendes, A.C.; de Araújo, B.M.; Barbosa, F.M. Aspectos do Pensamento Computacional Através da Cultura Maker e Aprendizagem Significativa: Um estudo de caso com mulheres iniciantes em cursos de tecnologia da informação. In Anais do XXXV Simpósio Brasileiro de Informática na Educação (SBIE 2024); Sociedade Brasileira de Computação: Porto Alegre, Brazil, 2024; pp. 3183–3190. [CrossRef] 27. Ariza, J.A. Bringing active learning, experimentation, and student-created videos in engineering: A study about teaching electronics and physical computing integrating online and mobile learning. Comput. Appl. Eng. Educ. 2024,31, 1723–1749. [CrossRef] 28. Sedubun, S.; Nurhayati, N. Exploring the Efficacy of Project-Based Learning in English Language Teaching: A Literature Review. EDUKASIA J. Pendidik. Pembelajaran 2024,5, 1089–1092. [CrossRef] 29. Tang, A. Implementing project-based language learning with adult multilingual learners of English. WAESOL Educ. 2023,48, 20–26. Available online: https://educator.waesol.org/index.php/WE/article/view/24 (accessed on 5 June 2025). 30. Da Silva Beraldo, A.L.; De Oliveira, T.; Stringhini, D. Laboratórios remotos e virtuais no Brasil com foco no ensino: Uma revisão sistemática da literatura. RENOTE 2021,19, 330–340. [CrossRef] 31. Silva, J.B.; Bilessimo, S.M.S.; Scheffer, G.R.; Nardi da Silva, I. Laboratórios Remotos como Alternativa para Atividades Práticas em Cursos na Modalidade EAD. EaD Em Foco 2020,10, 150. [CrossRef] 32. Maia, V.O.; Freire, S. A diferenciação pedagógica no contexto da educação inclusiva. Rev. Exitus 2020,10, 1–29. [CrossRef] 33. Bond, M.; Zawacki-Richter, O.; Nichols, M. Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology. Br. J. Educ. Technol. 2019,50, 12–63. [CrossRef] 34. Filho, M.A.S.A.; Ferreira, A.M.; Oliveira, C.X.; Boechat, G.P.F.; Carmo, J.P.G. Educação híbrida: Explorando a combinação de metodologias ativas presenciais e tecnologia no currículo. Cuad. Educ. Desarro. 2024,16, e4891. [CrossRef] 35. Santos Domingues, A.L.; Rossetto, H.H.P.; Junior, O.P.; Carvalho, D.F. Aprimorando o ensino superior por meio da avaliação formativa online: Estratégias e implementação. Rev. Prod. Educ. Pesqui. Ensino 2024,8, 1589–1607. Available online: https: //seer.uenp.edu.br/index.php/reppe/article/view/1555 (accessed on 4 June 2025). 36. Schmid, M.; Brianza, E.; Mok, S.Y.; Petko, D. Running in circles: A systematic review of reviews on technological pedagogical content knowledge (TPACK). Comput. Educ. 2024,214, 105024. [CrossRef] 37. Nikou, S.A.; Economides, A.A. Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance. Comput. Hum. Behav. 2017,68, 83–95. [CrossRef] 38. Gaševi´c, D.; Tsai, Y.S.; Dawson, S.; Pardo, A. How do we start? An approach to learning analytics adoption in higher education. Int. J. Inf. Learn. Technol. 2019,36, 342–353. [CrossRef] 39. Portillo, S.; Castellanos, L.; Reynoso, Ó.; Gavotto, O. Enseñanza remota de emergencia ante la pandemia Covid-19 en Educación Media Superior y Educación Superior. Propósitos Represent. 2020,8, e589. [CrossRef] 40. Chen, X.; Zou, D.; Xie, H.; Wang, F.L. Past, present, and future of smart learning: A topic-based bibliometric analysis. Int. J. Educ. Technol. High. Educ. 2021,18, 2. [CrossRef] 41. Santos, C.M.; Assumpção, G.S.; Castro, A.C. Dispositivos Educacionais, Tecnologias Digitais e os Desafios do Cenário de Transição para um Ensino Híbrido nas Engenharias. Rev. Humanid. Inov. 2023,9, 32–44. Available online: https://www.researchgate.net/ publication/369788228 (accessed on 5 June 2025). 42. Selwyn, N. Is Technology Good for Education? John Wiley & Sons: Hoboken, NJ, USA, 2016. 43. Sailer, M.; Maier, R.; Berger, S.; Kastorff, T.; Stegmann, K. Learning Activities in Technology-Enhanced Learning: A Systematic Review of Meta-Analyses and Second-Order Meta-Analysis in Higher Education. Learn. Individ. Differ. 2024,112, 102446. [CrossRef] 44. Zawacki-Richter, O.; Marín, V.I.; Bond, M.; Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education—Where are the educators? Int. J. Educ. Technol. High. Educ. 2019,16, 39. [CrossRef] 45. Cuban, L. Reforming the Grammar of Schooling Again and Again. Am. J. Educ. 2020,126, 665–671. [CrossRef] Sustainability 2025,17, 6429 19 of 21 46. Malta, D.P.L.N.; Santos, S.M.A.V.; Carvalho, E.O.; Sacramenta, G.A.O.; Silva, M.R.; Sacramenta, M.S.; Barp, O.C.; Rodrigues, S.C. Inteligência artificial e suas implicações na personalização do ensino: Desafios e oportunidades. Cuad. Educ. Desarro. 2025,17, e7372. [CrossRef] 47. Souza, A.P.S.; Conceição, C.J.; Pancoto, M.A.; Cecote, N.Q.B.; Pedra, R.R.; Oliveira, R.M.S.; Pinão, V.R.Z.; Gomes, W.T. Personalização da aprendizagem com inteligência artificial: Como a IA está transformando o ensino e o currículo. Rev. Aracê2024,6, 5816–5831. [CrossRef] 48. Okonkwo, C.W.; Ade-Ibijola, A. Python-bot: A chatbot for teaching python programming. Eng. Lett. 2020,29, 1. Available online: https://openurl.ebsco.com/EPDB:gcd:6:15007126/detailv2?sid=ebsco:plink:scholar&id=ebsco:gcd:148993113&crl=c& link_origin=scholar.google.com (accessed on 5 June 2025). 49. Sajja, R.; Sermet, Y.; Cikmaz, M.; Cwiertny, D.; Demir, I. Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education. Information 2024,15, 596. [CrossRef] 50. Silva Franqueira, A.; Malta, D.P.D.L.N.; dos Santos, F.J.; de Almeida, G.A.; da Silva, L.V.; da Cruz Silva, M.; Woodcock, Z.S.P. Inteligência artificial na personalização da aprendizagem. Obs. Econ. Latinoam. 2024,22, e4101. [CrossRef] 51. Sweller, J. Cognitive Load Theory and Teaching English as a Second Language to Adult Learners. Contact Mag. 2017,43, 10–14. Available online: http://contact.teslontario.org/wp-content/uploads/2017/05/Sweller-CognitiveLoad.pdf (accessed on 5 June 2025). 52. Ryan, R.M.; Deci, E.L. Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemp. Educ. Psychol. 2020,61, 101860. [CrossRef] 53. Tang, Y.; Hare, R. Combining gamification and intelligent tutoring systems in a serious game for engineering education. arXiv 2023, arXiv:2305.16568. [CrossRef] 54. Möller, M.; Nirmal, G.; Fabietti, D.; Stierstorfer, Q.; Zakhvatkin, M.; Sommerfeld, H.; Schütt, S. Revolutionising distance learning: A comparative study of learning progress with AI-driven tutoring. arXiv 2024, arXiv:2403.14642. [CrossRef] 55. Silva, M.C.F.; Saraiva, A.C.G.T.; Malta, D.P.L.N.; Silva, J.E.C.; Silva, R.F.; Santos, S.A. A integração de inteligência artificial na personalização do ensino: Um novo paradigma para a educação básica. Rev. Aracê2024,6, 5956–5972. [CrossRef] 56. Pedrosa, S.M.P.A.; Zappala-Guimarães, M.A. Realidade virtual e realidade aumentada: Refletindo sobre usos e benefícios na educação. Rev. Educ. E Cult. Contemp. 2019,16, 123–146. Available online: https://mestradoedoutoradoestacio.periodicoscientificos. com.br/index.php/reeduc/article/view/6258 (accessed on 5 June 2025). [CrossRef] 57. Anjos, F.E.V.d.; Martins, A.d.O.; Rodrigues, G.S.; Sellitto, M.A.; Silva, D.O.d. Boosting Engineering Education with Virtual Reality: An Experiment to Enhance Student Knowledge Retention. Appl. Syst. Innov. 2024,7, 50. [CrossRef] 58. Matos, C.C.; Coutinho, D.J.G. Desafios educacionais: A resistência do professor às novas tecnologias e a necessidade de capacitação. Rev. Ibero-Am. Humanidades Ciências Educ. 2024,10, 1069–1079. [CrossRef] 59. Radianti, J.; Majchrzak, T.A.; Fromm, J.; Wohlgenannt, I. A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Comput. Educ. 2020,147, 103778. [CrossRef] 60. Dede, C.J.; Jacobson, J.; Richards, J. Introduction: Virtual, Augmented, and Mixed Realities in Education. In Virtual, Augmented, and Mixed Realities in Education; Liu, D., Dede, C., Huang, R., Richards, J., Eds.; Springer: Singapore, 2017; pp. 1–16. [CrossRef] 61. França, C.R.; da Silva, T. A Realidade Virtual e Aumentada e o Ensino de Ciências. Educitec-Rev. Estud. E Pesqui. Sobre Ensino Tecnológico 2019,5, 193–215. Available online: https://sistemascmc.ifam.edu.br/educitec/index.php/educitec/article/view/414 (accessed on 9 June 2025). [CrossRef] 62. Luburi´c, N.; Slivka, J.; Dori´c, L.; Proki´c, S.; Kovaˇcevi´c, A. A framework for designing software engineering project-based learning experiences based on the 4 C/ID model. Educ. Inf. Technol. 2025,30, 1947–1977. [CrossRef] 63. Silva, C.R.T.; Vieira, G.; da Silva Filho, G.L.; Costa, G.S.D.S.; Gomes, I.S.R.; Miranda, M.M.C.; Rosa, Z.A. O uso da gamificação como estratégia no design instrucional. Rev. Ibero-Am. Humanidades Ciências Educ. 2024,10, 4168–4172. [CrossRef] 64. Teixeira, R.L.P.; Silva, P.C.D.; de Araújo Brito, M.L. Gamificação para o ensino de engenharia no contexto da indústria 4.0: Metodologia estratégica para a motivação dos estudantes. Rev. Casos Consult. 2021,12, e23964. 65. Nemer, E.G.; Ramirez, R.A.; Frohmut, B.D.F.; Bergamo, R.O.C. Um estudo de caso sobre o uso de gamificação e da realidade virtual na educação profissional. Rev. REFAS-Fatec Zona Sul 2020,6, 1–13. [CrossRef] 66. Santos, S.M.A.V.; Teixeira, M.L.L.D.; Silva, K.G.D.; Pereira, M.G.; Scholl, M.; Felicio, M.L.; Silva, W.L. Metodologias ativas: Como a gamificação, sala de aula invertida, e aprendizagem baseada em projetos se beneficiam das tecnologias digitais. Contrib. A Las Cienc. Soc. 2024,17, e10386. [CrossRef] 67. Freire, E.F.S.; Santos, R.P.; Silva, S.V. Gamificação em engenharia de software: Evidências do processo de ensino-aprendizagem. Educ. Temática Digit. 2024,26, e023003. Available online: https://dialnet.unirioja.es/servlet/articulo?codigo=9318027 (accessed on 5 June 2025). 68. Peng, J.; Yuan, B.; Sun, M.; Jiang, M.; Wang, M. Computer-based scaffolding for sustainable project-based learning: Impact on highand low-achieving students. Sustainability 2022,14, 12907. [CrossRef] Sustainability 2025,17, 6429 20 of 21 69. Moura, L.B.A.F.; Bossi, L.A.O.; Salume, P.K. Gamificação: Sua aplicação na educação e as implicações para o contexto do ensino de engenharia. Rev. Ensino Eng. 2021,40, 128–136. Available online: https://revista.abenge.org.br/index.php/abenge/article/ view/1790/1023 (accessed on 9 June 2025). 70. Rezende, A.A.; Carrasco, E.; Silva-Salse, À. Aprendizagem baseada em jogos e gamificação como instrumentos para o desenvolvimento do pensamento crítico na matemática: Uma revisão teórica. Rev. Estud. Em Educ. Divers. 2022,3, 1–18. [CrossRef] 71. Silva, J.B.; Sales, G.L.; Castro, J.B. Gamificação como estratégia de aprendizagem ativa no ensino de Física. Rev. Bras. Ensino Física 2019,41, 1–9. [CrossRef] 72. Segundo, A.H.F.N.; Neto, J.S.D.C.; da Silva, R.F.; Barbosa, P.C.S.; Santana, R.F. Development of a robotic manipulator: Applying interdisciplinarity in Computer Assister Project, Microcontrollers and Industrial Robotics. arXiv 2022, arXiv:2203.16924. [CrossRef] 73. Cucuzzella, C.; Hazbei, M.; Asgari, M.H. Parametrizing the Unmeasurable: Urban Qualities as Quantitative Parameters for Computer Games. Int. J. Archit. Comput. 2024,22, 412–431. [CrossRef] 74. Almeida, F.V.; Hayashi, H.T.; Carrer, A.M.; Arakaki, R. HomeLab: Levando o Laboratório até a Residência do Aluno. Rev. Bras. Informática Educ. 2023,31, 712–730. Available online: https://journals-sol.sbc.org.br/index.php/rbie/article/view/2807 (accessed on 4 June 2025). [CrossRef] 75. Roque, G.R.; Simão, J.P.; Bilessimo, S.M.S.; Silva, J.B.; Neto, J.M.; Izidoro, C.L. Experimentação remota no ensino de superior: Linguagens de programação nas engenharias mecatrônica e automação industrial. Rev. Ensino Eng. 2017,36, 96–105. Available online: https://revista.abenge.org.br/index.php/abenge/article/view/1354/789 (accessed on 4 June 2025). 76. Carvalho, M.; Figueiredo, J.N.; Cavalcanti, G.C.D.; Freire, R.S.; Machado, L.S.; Abrahão, R. Educação ambiental por meio de um app para quantificação de pegada de carbono. Res. Soc. Dev. 2021,10, 1–16. [CrossRef] 77. Tawfik, A.A.; Shepherd, C.E.; Gatewood, J.; Gish-Lieberman, J.J. First and Second Order Barriers to Teaching in K-12 Online Learning. TechTrends 2021,65, 925–938. [CrossRef] 78. Carvalho Ribeiro, D.B.; da Silva Rodrigues, A.M.; Costa, M.S.; de Oliveira Gonçalves, A.K.; Lima, M.I.B. Inovação no ensino e aprendizagem de empreendedorismo através da gamificação: O planejamento da experiência de aplicação do Classcraft no ensino médio integrado ao técnico de administração. An. CIET Horiz. 2020,5, 1–12. Available online: https://ciet.ufscar.br/ submissao/index.php/ciet/article/view/938 (accessed on 8 June 2025). 79. Henriques, S.; Neves, C.; Silva, A.P.; Abrantes, P.; Ramos, M.D.R.; Jacquinet, M.; Bäckström, B.; Falé, I.; Magano, O. Literacia e inclusão digital no ensino superior online: Impactos em adultos diplomados. Sociol. Probl. Práticas 2023,101, 29–51. [CrossRef] 80. Burgstahler, S. Creating Inclusive Learning Opportunities in Higher Education: A Universal Design Toolkit; Harvard Education Press: Cambridge, MA, USA, 2020. Available online: https://hep.gse.harvard.edu/9781682535400/creating-inclusive-learningopportunities-in-higher-education/?utm_source=chatgpt.com (accessed on 11 June 2025). 81. Corbí, M.; Rodríguez-Cano, S.; Cuesta-Gómez, J.L.; Morais-Barcina, P.; Merino, M.; Gómez-Gentil, M. Ideal project-inclusive digital education for autistic people learning. In INTED2023 Proceedings; IATED: Valencia, Spain, 2023; p. 7881. Available online: https://library.iated.org/view/CORBI2023IDE (accessed on 18 June 2025). 82. Durlak, J.A.; Mahoney, J.L.; Boyle, A.E. What we know, and what we need to find out about universal, school-based social and emotional learning programs for children and adolescents: A review of meta-analyses and directions for future research. Psychol. Bull. 2022,148, 765–782. [CrossRef] 83. Valente, S.; Lourenço, A.A.; Dominguez-Lara, S. Teachers in the 21st Century: Emotional Intelligence Skills Make the Difference. In Pedagogy—Challenges, Recent Advances, New Perspectives, and Applications; ¸Senol, H., Ed.; IntechOpen: London, UK, 2022; pp. 1–15. Available online: https://www.intechopen.com/chapters/80831 (accessed on 18 June 2025). 84. Lipson, S.K.; Lattie, E.G.; Eisenberg, D. Increased Rates of Mental Health Service Utilization by U.S. College Students: 10-Year Population-Level Trends (2007–2017). Psychiatr. Serv. 2019,70, 60–63. [CrossRef] 85. Salmon, G. E-Moderating: The Key to Teaching and Learning Online; Psychology Press: London, UK, 2004. Available online: https: //www.taylorfrancis.com/books/mono/10.4324/9780203816684/moderating-gilly-salmon?utm_source=chatgpt.com (accessed on 11 June 2025). 86. Li, J.; Cochrane, K.A.; Leshed, G. Beyond Meditation: Understanding Everyday Mindfulness Practices and Technology Use Among Experienced Practitioners. Proc. ACM Hum. Comput. Interact. 2024,8, 1–29. [CrossRef] 87. Poo, M.C.P.; Lau, Y.Y.; Chen, Q. Are Virtual Laboratories and Remote Laboratories Enhancing the Quality of Sustainability Education? Educ. Sci. 2023,13, 1110. [CrossRef] 88. Damasceno, E.; Santana, A.C.A.; Pinheiro, L.C.; Santos, C.H.A.; Alves, D.L.; Figueiredo, S.A.; Vieira, G.B.D.; Klauch, J.J.; Pires, R.R.; Costa, L. A Importância da Educação Ambiental na Era Digital. J. Humanit. Soc. Sci. 2024,29, 55–62. Available online: https://www.iosrjournals.org/iosr-jhss/papers/Vol.29-Issue5/Ser-10/J2905105562.pdf (accessed on 3 June 2025). 89. Abdillah, L.A.; Rofiq, A.A.; Indriani, D.E. Information technology utilization in environmentally friendly higher education. arXiv 2018, arXiv:1811.10856. [CrossRef] 90. Martínez Valdivia, E.; Pegalajar Palomino, M.D.C.; Burgos-Garcia, A. Active methodologies and curricular sustainability in teacher training. Int. J. Sustain. High. Educ. 2023,24, 1364–1380. [CrossRef] Sustainability 2025,17, 6429 21 of 21 91. Patiño, A.; Ramírez-Montoya, M.S.; Buenestado-Fernández, M. Active learning and education 4.0 for complex thinking training: Analysis of two case studies in open education. Smart Learn. Environ. 2023,10, 8. [CrossRef] 92. Machado, C.F.; Davim, J.P. Sustainability in the Modernization of Higher Education: Curricular Transformation and Sustainable Campus—A Literature Review. Sustainability 2023,15, 8615. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.