Today, more than ever, in a market oriented towards sustainability, inclusion, and new technologies, it is necessary to integrate educational and guidance systems with tools that enable future professionals to choose their career path and later contribute to the collective good. To foster good career choices for engineers we have developed the COACH_ING model presented in earlier papers. In this paper we want to present a project and an experiment, conducted at an engineering faculty of a European university as part of a country wide Guidance and Tutoring Project. In our COACH_ING model, used by the faculty and professional associations for over seven years, new tools have been implemented for conscious career choice guidance and the enhancement of one's professional potential, via future-oriented educational concepts. The activities took place in structured workshops during which, assessments and questionnaires were systematically administered to gather useful research data and monitor the development of reference skills drawn from coaching and engineering. Specifically, the tool DIRECTING, presented at the EFEA2021, now called DIRECTINGVer2, has been enriched, as a questionnaire, with new elements based on empirical validations performed in the meantime. The collected data provided new insights into the level of learning of specific reference parameters related to the$\mathbf{C O A C H \_ I N G}$model intersecting Dublin Descriptors as a broadly known framework for qualification in degree programs. In the individual interventions, classroom techniques specific to new guidance models were respected, with the aim of developing an open mindset in students, through conditions initially defined based on the mastery of 16 skills and 8 MCs (Macro Capabilities) of the COACH_ING model. During our not-traditional lab, data emerged regarding eight MCs and four clusters of the questionnaire. Participants were involved in group work to bring out their knowledge and potential (as presented at ICL2024). Through focus groups and 1-to-1 sessions, we verified the actual level of confidence and acquisition of knowledge related to both the MCs and the dimensions identified in the Clusters: communication, teamwork, goal and project, relationship. The model finds its foundations in coaching process engineering, building a life project and choosing one's personalized future path. There emerges a strong need to focus on the advancement of engineering education and the concepts to be conveyed to future engineers so that they can act for their own benefit and that of the surrounding world, making conscious choices.

Artificial Intelligence (AI) represents one of the most rapidly developing fields of the current era, with its applications already supplementing various aspects of everyday life. But, with this ever-widening adoption, concerns arise regarding the processes behind its decision-making and the fairness of the produced results. This paper uses the Restricted Systematic Review format to investigate the relationship between AI, women, and engineering education. The review, conducted from IEEE Xplore Conference Papers and Journal Articles from 2010 to 2024, seeks to indicate the evolution of the subject over the designated period, the various ways AI is used in different aspects of education, the gender-related biases and discriminative outcomes that can be inadvertently replicated, but also how this technology can be deployed to advance equity. Initially, 279 papers were retrieved from IEEE Xplore, of which 111 were considered potentially relevant and were read. Finally, 34 papers were found to be relevant and were thus synthesized for this review. The results support the potential of AI in educational processes, a part of which is already being actively researched, especially regarding learning analytics and outcome prediction. In addition, the review confirms that researchers are becoming aware of the gender-based issues that can occur based on historical evidence and have begun proposing some potential actions to mitigate them. The review aims to help stakeholders further explore and leverage the capabilities of AI to foster an inclusive engineering education field for both students and teachers by identifying under-researched areas and applications.

In the context of women's integration into the working domain, the gradual reduction of the gender gap across numerous professional fields continues to be a reality. However, it is imperative to underline that the attainment of full gender equity across professional domains, including engineering, remains an unfulfilled objective. This document is aimed at presenting a general view of the gaps regarding women in engineering through a literature revision of the works included in the proceedings of IEEE EDUCON, to identify the factors that may still underlie the low representation of females in engineering and provide guidance to this conference stakeholders for upcoming studies and publications on this topic. The results indicate that factors such as entry and affordability hurdles, inadequate training and built-in prejudices, along with prevalent sociocultural norms, constrain greater diversity and inclusiveness of females in engineering majors. As such taking action now aiming to eliminate the gender gap by reversing these trends may result in the desired outcome. It may also provide new sources of global economic growth and support the implementation and achievement of sustainable development and inclusive growth. Finally, the literature review highlights the importance of going beyond the diagnostic phase while identifying lines of work to be strengthened.

The current teaching methodologies in engineering education aim to provide engineering students with comprehensive, interdisciplinary training. Companies increasingly assess not only technical skills but also transversal skills when evaluating their resources. This paper aims to the integration of assessing and monitoring new skills throughout students' educational and professional journeys. It emphasizes the importance of making students and teachers aware of these skills, even when their primary focus remains on specific subject areas. To achieve this, we employed reliable research tools, collected data representative of our target population, and implemented specialized tools designed to assist engineering students in choosing the most suitable professional paths. To enhance the responsiveness to the prevailing demands of the job market, our research has been concentrated on the improving communication skills of engineering students at the University of Rome “Sapienza” through educational personalized programs. The research had the COACH_ING training model, an integration of coaching and engineering principles, as main program guide. This model was developed over 10 years of experiments among members of the COUNCIL of Engineers of Rome and University of Rome “Sapienza” involving several private high-tech companies. This model equips undergraduate engineering students with communication methodologies designed for self-improvement. As a result, we have gathered data that allows us to optimize university labs for students, helping them master communication skills and become more effective in presenting themselves in the job market.

The COACH_ING model is an educational framework where engineering skills and soft behavioral skills integrate in a new set of competencies to train students and young technicians in developing their readiness to enter the job market, with strong relational skills and awareness of the career path best suited to their skills and growth potential. The method uses a set of evaluation forms, direct-assessment and a multiple-choice questionnaire to analyze the level of confidence with technical engineering and relational coaching skills to design a training path for any participant. This analytical approach will be used to assess and train multifactorial skills and personal attitude to meet specific professional families in engineering field. We have already received various confirmations of the accuracy of our analyses on the profiling of professional families and we intend to expand the research to a greater number of subjects to refine our tool-set and enhance the models’ effectiveness.