Industry 4.0 Skills Assessment: A Case Study of Students’ Perceptions in Computer Science Postgraduate Programs

Abstract

With the appearance of Industry 4.0, the need for highly competent professionals in disruptive technologies and emerging intelligent computing is undeniable. In this way, education plays a crucial role in the success of sustainable development initiatives, as it must effectively integrate innovative practices, knowledge assurance, and new technologies. Thus, educational institutions must adjust the contents of their study plans to ensure that their graduates can successfully integrate into this constantly evolving work environment. In this paper, we present a study that identified how students at a computing research center perceived the relevance of the competencies and skills acquired during their studies to face the challenges of Industry 4.0. A survey was designed with 29 questions applied to a sample of 112 students of the Centro de Investigación en Computación, IPN. The results were analyzed statistically, and an inferential analysis concluded that the research hypothesis must be accepted. This study contributes to the global discussion on sustainable educational systems (SDG 4), demonstrating that student perception of their competencies for Industry 4.0 is mediated by integrating ethical and environmental principles in the curricula, a critical factor in Latin American contexts.

1. Introduction

Today’s world is characterized by increasing competition and constant evolution, driven by the appearance of Industry 4.0 and the integration of digital and disruptive technologies such as artificial intelligence, big data, automated robotics, cybersecurity, the Internet of Things (IoT), cloud computing, and additive manufacturing. Not only have these technologies modified production processes and economic models, but they also pose fundamental challenges to sustainability, equity, and social responsibility, requiring a workforce trained in technical skills and competencies that foster sustainable development.

In this context, the undeniable need for highly competent professionals in these disruptive technologies and emerging intelligent computing is undeniable. To face this challenge, higher education plays a crucial role in the training and preparation of highly qualified professionals in these fields [1]. Educational institutions must adjust the contents of their study plans to ensure that their graduates can successfully integrate into this constantly evolving work environment, which requires curricular updates and infrastructure improvements.

Education plays an essential role in the success of sustainable development initiatives, as it must effectively integrate innovative practices, knowledge assurance, and new technologies. Education for Sustainable Development (ESD) is fundamental to addressing these challenges, aligning with the United Nations Sustainable Development Goals (SDGs). Specifically, SDG 4 underscores the importance of quality education for training professionals capable of driving a sustainable, fair, and equitable future, and SDG 4.7 emphasizes the need for all students to acquire the knowledge and skills necessary to promote sustainable development [2], including understanding the interconnections between economic, social, and environmental systems [3]. New digital technologies are essential tools for achieving the SDGs, but their implementation must be accompanied by education that fosters responsibility, ethics, and sustainable innovation.

The future workforce requires not only technical skills in Industry 4.0-enabling technologies but also soft skills such as communication, teamwork, adaptability, leadership, critical thinking, creativity, and collaboration, as well as digital competencies. These skills are essential for addressing global challenges and can be used to design innovative and sustainable solutions that contribute to social and environmental well-being. Education must be holistic, preparing students to understand the social, environmental, and ethical implications of innovation. To achieve this, universities must collaborate closely with industry and other stakeholders to update curricula and include projects and practical experiences that connect academic knowledge with the real world.

In a World Economic Forum report, seven groups of technological fields were identified [4]. These technological fields are blurring the boundaries between global production systems’ physical, digital, and biological spheres. They drive industry transformation through innovations focused on connectivity and computing, analytics and intelligence, digital and physical transformation, production philosophies, advanced materials, manufacturing processes, and human–machine interfaces. These transformative fields impact various aspects of global production systems and classifies technologies as mainstream, maturing, or emerging. These elemental features must support the efforts to enrich the specialized readiness of technologies and ensure the evolution of a proficient workforce committed to promoting responsible practices. This approach is essential for industries, businesses, governments, and educational institutions to integrate innovative solutions that balance technological progress with environmental protection, social equity, and sustainable economic development.

Thus, considering the technological fields recognized by [4], science and technology education cannot depend solely on predefined study programs designed by universities and research centers. While these institutions play a crucial role in providing fundamental knowledge and theoretical frameworks, the rapid evolution of global industries, technological advances, and societal challenges demand that educational programs remain fluid and responsive [5]. These programs must continually adapt and realign with the emerging needs of society and the ever-changing demands of the innovation and industry ecosystems, which are increasingly shaped by the advances brought about by the four industrial revolutions.

This study aims to assess the feasibility and relevance of postgraduate human resource training in computing and engineering to meet the needs of the country’s educational, productive, and service sectors, with a focus on sustainability. While programs are updated to ensure quality, a critical aspect is preparing students for the innovation required by Industry 4.0, integrating social, environmental, and ethical considerations that foster sustainable development.

In the current technological landscape, innovation and industry ecosystems increasingly demand specialists proficient in fourth industrial revolution (IR4.0) technologies.Against this backdrop, the proposed investigation aspires to assess the extent to which students perceive their master’s-level training as preparing them for successful integration into sustainability-focused innovation and industry environments.

The main study objective of this research is to evaluate, through statistical analysis, students’ perceptions of the master’s programs offered at the Centro de Investigación en Computación of the IPN, concerning adequate training and the acquisition of knowledge and skills that allow them to integrate into innovative and sustainable work environments. Specifically, this study seeks to measure the alignment between acquired competencies and the requirements of Industry 4.0 with a sustainable focus and to identify priority curricular areas to improve employability with better technically qualified graduates. To address this issue, a 29-item survey was designed and administered to 112 students at the Computing Research Center. The instrument assessed the following:

• The perceived alignment between acquired skills and industry requirements in digital technologies.
• The students’ self-perceived level of preparedness to integrate into innovation ecosystems driven by IR4.0.
• The integration of sustainability principles into their technical training.

In addition, the survey used Likert scales and statistical analysis to correlate variables with the results. This analysis generated empirical evidence on the gaps between academic training and the real needs of the productive sector in Mexico, whose objective is emerging technologies and sustainability (SDG 9).

Furthermore, the research seeks to provide curriculum designers with concrete input to update curricula, focusing on responsible innovation and strengthening the teaching of soft skills (leadership, ethics) linked to sustainable development (SDG 4). Similarly, this study attempts to establish a framework for future studies on STEM education and its role in the transition to 5.0 societies.

This paper is structured as follows: Section 2 presents a literature review concerning the state of the art. Section 3 describes the methods and materials to analyze the relevance of the competencies developed for their insertion in the innovation and industry ecosystems. In addition, it includes the research hypothesis and the methodology developed. Section 4 presents the results obtained by applying the methodology. Moreover, the discussion related to the research findings is described in Section 5. Finally, Section 6 presents the conclusions of this research study and future work.

2. Related Work

Postgraduate education plays a fundamental role in qualifying highly trained and updated professionals to meet the challenges of the disruptive technologies that have emerged from the fourth industrial revolution. The knowledge and skills imparted at this level are essential for success in this context [6].

Several studies highlight the importance of universities adapting their curricula [7]. Educational institutions must adjust their curricula to ensure graduates can successfully integrate into this constantly evolving work environment [8]. This requires integrating innovative practices and new technologies [9]. Universities play an important role in preparing future graduates for challenges in the workplace and in helping students understand the changing demands of industry and the skill sets they need [8].

Universities must understand industry demands and introduce new programs that prepare students with the necessary competencies [9]. This preparation must consider Education for Sustainable Development. Studies exist on sustainable development in the university context [10]. Examining how students evaluate university initiatives and the individual pillars of sustainable development is an area of focus [7].

Research has focused on identifying the specific skills required by Industry 4.0. Digital technologies such as artificial intelligence, big data analytics, cybersecurity, robotics, IoT, cloud computing, and online education are identified as important for strengthening student skills in the teaching–learning process. The importance of soft skills is also emphasized. Universities should gear their teaching materials and classes towards helping students understand these changing demands and the necessary skill sets.

Studies have also examined the gap between academic training and the needs and requirements of Industry 4.0. It is crucial to understand how students perceive the competencies acquired during their studies, considering the labor challenges posed by innovation and industry ecosystems. Understanding students’ perception of the relevance of competencies for integration into environments that prioritize sustainable innovation is relevant. As mentioned by Avalos-Bravo et al. [11], the transformation of education (Education 4.0) requires responding to the needs of in-demand talent, academic plans, and programs, which must be modernized and developed so that they can react to the productive sector in the face of the fourth industrial revolution, highlighting education for sustainability and sustainable innovation.

In its 2020 report on the future of jobs [12], the World Economic Forum presented a labor market forecast for 2020–2025. The analysis focused on 14 industrial sectors across 26 countries, including emerging and advanced economies. In addition, this study conducted a survey to address several areas related to expected labor transformations, emphasizing the importance of sustainability in the training of professionals. Thus, the study design included questions on critical trends affecting the labor market, adopted technologies, required skills, and the need for employee training and skill-updating programs. The results revealed companies’ rapid adoption of IT technologies, increasing demand for a highly skilled workforce capable of meeting their needs, and a focus on sustainable innovation. Figure 1 presents the percentage value of each sector according to the application of communication and information technologies.

In the World Economic Forum’s 2020 Future of Jobs Report [12], the results from each of the 26 selected countries were also presented. Regarding Mexico specifically, two significant findings can be highlighted, particularly involving the objectives of this study: (1) the rate of technology adoption corresponds to 73% in the artificial intelligence field, and (2) the demand for specialized jobs is concentrated in specialists in artificial intelligence and machine learning, data analysis, big data, information security, process automation, digital marketing and strategy, FinTech engineers, architects and surveyors, and university and higher education teachers.

The most significant challenge faced by higher education institutions arising from the COVID-19 pandemic was the abrupt transition to online teaching. HEIs continually confront the difficulties of creating satisfactory online learning experiences that comply with new regulations based on sustainable principles. Thus, Lytras et al. [13] presented a study about the perception of quality and efficiency in the educational process in higher education, specifically concerning distance education at the Instituto Politécnico Nacional of Mexico during the COVID-19 pandemic. The data analysis focused on evaluating student and teacher satisfaction with distance education and their lived experience for potential future educational models, considering sustainable innovation. They used a stepwise regression model to identify the most relevant variables related to satisfaction with online learning among students and teachers. The analysis revealed that the perception of their abilities was the most appropriate variable in determining satisfaction with online learning, which implies knowledge of e-learning platforms and adaptation to new technologies. According to the authors, the primary contribution of their research was translating knowledge into a sustainable innovation capacity model, which can be applied to contexts of education for sustainability. Moreover, another study examined the consequences of implementing distance learning in different cultural contexts by analyzing social media posts, online classes, and interviews [14]. The study highlighted that the quality of the educational process could be negatively affected by inadequate preparation and experience, which are associated with academic and psychological implications. Additionally, other factors, such as procedural or logistical challenges, as well as general limitations of “stay-at-home” measures (e.g., stress, anxiety, among others), hindered the ability of students and teachers to learn and teach effectively. These findings highlight the importance of designing effective, sustainable, and equitable online educational models, promoting education for sustainability even in crisis contexts.

Other case studies, such as Iglesias-Pradas et al. [15], examined the transition to remote teaching in emergency situations and its organizational impact. The study considered unplanned changes, class sizes, delivery methods (synchronous or asynchronous), and the utility of digital support technologies focused on sustainability. The findings indicated an improvement in students’ academic performance during the remote teaching emergency and supported the idea that addressing organizational issues can facilitate the successful implementation of online education models, promoting sustainable innovation in higher education. Other investigations examined the perception of distance education in different countries at the onset of the transition to online during the COVID-19 pandemic, including Poland [16], Portugal [17], Greece [18], Germany [19], Morocco [20], Nigeria [21], worldwide with a focus on Asia [22], Arab countries [14,23], China [24], Indonesia [25,26], Libya [27], and Canada [28]. These studies underline the importance of adapting educational models to local needs and promoting education for sustainability and sustainable innovation in diverse contexts.

Halili and Sulaiman [29] utilized a qualitative method, specifically interviews, to collect data from a small sample of six students (two males and four females). This qualitative analysis uses words instead of numbers and is considered appropriate for a small sample size.

Another study [7], by Deák and Kumar (2024), examined the role of STEAM pedagogy in enhancing digital skills and fostering a workforce capable of addressing the challenges posed by sustainable innovation. This systematic review focused on the literature from 2013 to 2023 using the keywords “Digital competence” OR “sustainable innovation” in the Web of Science (WoS) and Scopus databases. The review followed the PRISMA framework to ensure methodological rigor, transparency, and systematic synthesis. The PRISMA framework provides a standardized method for the selection, choice, and evaluation of the literature, promoting transparency in reporting. They conceptualize “digital competencies” as a multifaceted paradigm encompassing technical proficiency, critical evaluation, and a motivated engagement with digital technologies. Technical proficiency is not the sole focus. A phase of qualitative analysis was part of the investigation. The identified Needs (N) in the NOISE model involve constructing a framework covering the development of all 22 designated digital skills for innovation. The European Framework for the Digital Competence of Educators (DigCompEdu) is mentioned as a reliable scientific framework outlining what it means for educators to be digitally competent. It lists 22 competencies organized into six categories, focusing on how digital technologies might be used to innovate and improve training and education. The study highlights the challenges that educators face in equipping students with the necessary digital competencies for future sustainable innovations. Barriers found in a study in Spain included technophobia, time constraints, insufficient planning, lack of incentives, challenges in evaluation, work saturation, and the prevailing university accreditation model. The review also recognizes limitations, such as the specific time frame and the main focus on “digital competence” as a keyword. Potential biases in study selection and framework design are also noted. Practically, the study suggests that universities and educational institutions should invest in continuous training, hiring tech experts, and adopting STEAM-based curricula to enhance the digital competence of educators.

Fuertes et al. [6] proposed an educational environment based on Industry 4.0. This environment incorporates new enabling technologies associated with this new industrial revolution, such as connectivity with standard protocols, storage and data processing in the cloud, machine learning, digital twins, and industrial cybersecurity measures. It aims to reproduce realistic industrial conditions and includes components such as an industrial firewall, a VPN for cybersecurity, and an IoT gateway. The environment allows students to consolidate their theoretical learning through experimentation with real equipment. It is designed for hands-on tasks linked to a wide range of technologies, without losing focus on the complete system integration. The environment can be adapted for courses requiring an introductory or global view of Industry 4.0 or to train students with a strong background in automation or information technologies by defining tasks oriented towards detailed configuration rather than system operation. The educational experience was evaluated by the students through an anonymous survey and was found to be useful from both the students’ and faculty’s point of view.

Finally, Gao et al. [8] examined employers’ expectations regarding future graduates in the construction industry in Singapore for Industry 4.0. The study highlighted the importance of aligning students’ skill sets with market needs. It adopted a sequential explanatory design using mixed methods, beginning with a quantitative survey followed by qualitative interviews. Two sets of survey questions were prepared, targeting employers and final-year students. Employers evaluated the value of nine soft skills. The study found that the reasons for the high value of six of the nine soft skills included their direct benefits, the need to remain relevant in one’s job, and the nature of the construction industry, which requires teamwork. The study focused on final-year students from a specific program at NUS. Limitations include the inability to apply the questionnaire to other institutions and the potential differences in needs between the various types of jobs in the industry. The study suggests that future research with larger and more representative samples would be useful. Soft skills are identified as essential by employers

Table 1. Comparison between some works in the state of the art and our proposed study on technological skills acquired for Industry 4.0.

Study	Country	Methodology	Sample	Technology Assessment	Sustainability	Findings
Fuertes et al. (2021) [6]	Spain	Experimental + survey.	20 students	IoT, cloud computing, robotics, cybersecurity, connectivity	Not considered	Lack of practical laboratories
Low et al. (2021) [8]	Singapore	Likert scale surveys and interviews. Quantitative and qualitative analysis. Gap scores and t-test.	Survey of 30 final-year PFM students from the University of Singapore and survey of 30 employers, some interviews	Soft skills, artificial intelligence, big data	Not considered	Insufficient soft skills
Bongomin et al. (2020) [1]	Uganda	Systematic literature review.	70 state-of-the-art articles	Digital and soft skills	(ODS 9)	Gap in digital and soft skills
Deák & Kumar (2024) [7]	Hungary/ India	Systematic literature review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)/ NOISE.	144 studies	Digital skills	(ODS 4)	Digital skills gap: unprepared teachers and sustainability
Lytras et al. (2022) [13]	Greece/ Mexico	Survey, Likert scale. t-test. Multiple regression.	3200 students 840 professors	Digital platforms and distance learning	(ODS 4)	Gap in online education
Halili & Sulaiman (2021) [29]	Malaysia	Interviews on the Technology Acceptance Model (TAM). Perceived usefulness (PU) and perceived ease of use (PEOU). Qualitative approach.	6 students	General perception of digital skills and necessary facilities	Not considered	Gap in digital skills and necessary facilities
Rosak-Szyrocka et al. (2022) [10]	Pakistan	Opinion surveys and Likert scales. Logistic regression, means, and gap scores.	115 students	STEM, digital competence, and sustainability	(ODS 4)	Online transition for key Industry 4.0 courses
Benis et al. (2021) [9]	Israel	Survey of student leaders of the Bachelor of Industrial Engineering and Management (IEM) program at the Faculty of Industrial Engineering and Technology Management (IETM) at the Holon Institute of Technology (HIT).	No values are presented	The perception of digital competence and curricular adaptations was evaluated	Not considered	Online transition for key Industry 4.0 courses
Our proposed study (2025)	Mexico	Survey. Qualitative analysis. Descriptive and inferential statistics. t-test. Pearson correlation.	112 students	Artificial intelligence, IoT, cloud computing, big data	(ODS 4, 9)	Gap in sustainability and technological skills

systematizes eight key studies directly related to the proposed work. It demonstrates that ours is the only one to simultaneously assess technological competencies and their alignment with SDGs 4 and 9 in Mexican research postgraduate programs.

3. Methods and Materials

3.1. The Case Study

For this research study, the master’s programs offered at the Centro de Investigación en Computación, IPN of Mexico, were chosen as the case study. These academic programs are the Master of Science in Computer Science, focused on scientific research in emerging technologies, with a population of 89 students, of which 67 represent the sample, and the Master of Science in Computing Engineering, oriented to development of systems and applications for Industry 4.0, with a population of 48 students, of which 45 are the sample. These programs aim to prepare specialists capable of conducting scientific research and generating technology in information and communication technologies to solve engineering problems involving the design, analysis, and implementation of devices, systems, and processes. They are also expected to innovate, develop, and apply new technologies in computer science while leading work teams to tackle public and private problems. According to the issue, it is critical to identify students’ perceptions of the master’s programs in our case study.

3.2. Research Questions and Hypothesis

Given the relevance of fields such as artificial intelligence, big data analysis, nanotechnology, cybersecurity, blockchain, robotics, IoT, cloud computing, and online education, the following research questions arise: How do students perceive the relevance of the competencies developed during their master’s studies in enabling their successful integration into these fast-paced innovation and industry ecosystems? Are the skills acquired through the master’s programs offered at the research center sufficient to meet the demands of these industries?

Thus, these questions lead to the following hypotheses ($H_0$): Master’s students perceive that their acquired knowledge, while theoretically valuable, does not fully develop the technical, adaptive, or interdisciplinary competencies demanded by Industry 4.0 innovation ecosystems. ($H_1$): Master’s students perceive that their acquired knowledge is sufficient to accomplish the demands of the Industry 4.0 innovation ecosystems.

Although these programs offer a strong foundational understanding of the core principles within their disciplines, students may lack hands-on experience or exposure to the latest trends, tools, and technologies shaping the future of these fields. Moreover, they may perceive a gap between academic learning and the practical, real-world applications that are recently required by Industry 4.0.

Therefore, exploring this perception is crucial for research centers and educational institutions aiming to refine their curricula, ensuring students have theoretical knowledge and the practical skills and competencies needed to stand out in their careers. Understanding students’ perspectives on the adequacy of their training can improve educational programs, enabling stronger connections between academia and industry and ensuring that graduates are better prepared to thrive in today’s competitive job market.

3.3. Design of the Experiment

The theoretical foundations described previously provide the approach to understanding how master’s students at the research center perceive the competencies and skills acquired during their postgraduate studies in innovation ecosystems and Industry 4.0. These concepts assist in enhancing educational programs to integrate students successfully into this technologically advanced environment. Figure 2 depicts the proposed methodology to address the problem statement.

As a part of the methodology, we made a statistical analysis, emphasizing descriptive, correlational, and inferential examination, concerning the results to describe the distribution of variables, correlate some of them, and assess hypothesis testing to determine if the population hypothesis aligns with the data obtained from the sample. We defined the approach in four stages for the experiment. First, the research design corresponds to the definition of a non-experimental cross-sectional study executed with descriptive, correlational, and inferential scopes to measure the perception of master’s students at the research center regarding the competencies and skills acquired during their postgraduate studies. It is crucial to mention that our study deliberately adopted a non-experimental, cross-sectional approach without control groups because the primary objective was to explore student perceptions of the curriculum, not to prove causality. The “naturalness” of the context was essential for the findings. As Tisdell (2025) [30] argued, when the objective is to characterize a phenomenon in its natural context—in our case, student perceptions at the CIC-IPN—non-experimental designs are optimal for the following reasons: (1) they preserve the ecology of the real educational environment, (2) they allow for exploring complex variables, such as curricular relevance, that would be impossible to isolate experimentally, and (3) they generate authentic insights for institutional decision-making.

In this way, we established a matrix with operational variables based on our research questions and problem. Thus, the problem statement was defined as follows: “Given that innovation and industry ecosystems require attracting and retaining talent from highly qualified computing specialists in information technologies such as artificial intelligence and machine learning, big data, cybersecurity, software development, networks and communications, robotics and automation, IoT, and cloud computing, what is the perception of students regarding the relevance of the competencies developed during their master’s studies for successful integration into innovation and industry environments?”

On the other hand, given the relevance of these fields, the following research questions arise: How do students perceive the significance of the competencies developed during their master’s studies in enabling their successful integration into these fast-paced innovation and industry ecosystems? Are the skills students acquire through the master’s programs offered at the research center satisfactory to meet the demands of these industries?

Moreover, we considered two variables: the first one is the “master’s in computer science study plan”; its conceptual definition is “A set of teachings and practices that, with a specific arrangement, must be completed to fulfill a course of study or obtain a degree”. The second is “students’ perception of the relevance of the competencies developed during their graduate studies”; its conceptual definition is “relevance is the ability of educational institutions and education systems to provide concrete and viable responses, based on their nature and purposes, to the needs of society” [31]. As a result of the analysis, the dimensions, indicators, and items are shown in

Table 2. Dimensions, indicators, and items of the variable “master’s in computer science plans”.

Dimensions	Indicators	Items
Content. Topics, concepts, skills, and specific knowledge that are taught in the program. It defines what students are expected to learn and understand.	Coherence	Are the courses in the program organized logically and sequentially?
	Relevance	Do you consider the program contents relevant to the educational objectives?
	Updating	Do the contents reflect the advances and changes in the field of study?
Objectives and competencies. There may be different educational objectives for study programs, such as the development of specific skills (in addition to academic knowledge, study programs may focus on the development of transversal skills like critical thinking, problem-solving, effective communication, and teamwork), holistic education, and the acquisition of in-depth knowledge. What should students be able to do upon completing the program?	Clarity	Do you know what the objectives of the program are?
	Scope	What skills are expected to develop after completing the study program?
Evaluation. How is the student’s progress and achievement measured? It includes evaluation methods such as exams, assignments, projects, presentations, and other ways to assess learning.	Diversity of evaluations	Are different types of evaluations (exams, assignments, projects, presentations) used to measure your learning?
	Authenticity	Do the evaluations reflect real and contextual situations related to the field of study?
Resources and materials. It refers to the necessary resources for the program, such as study materials, laboratories, libraries, technology, teaching staff, and more.	Availability	Are the study materials, laboratories, libraries, technology, and teaching staff easily accessible?
	Updating	Are updated and relevant resources used to support learning?
Pedagogical approach. Some study programs may have a more practical approach, while others may be more theoretical or research-oriented.	Approach	Do you know what the approach of the graduate study program is?
Job and professional perspective. The program prepares students for the workforce, including acquiring relevant skills, professional internships, and potential job opportunities.	Preparation for the future	Did the program allow the development of skills and knowledge relevant to labor or professionals specialized in their field?
Collaboration with industry. Learning outcomes are supported by collaboration.	Relationship between the program and the industry, joint projects	Does the graduate program include projects or practical activities focusing on industry applications?

and

Table 3. Dimensions, indicators, and items of variable “students’ perception of the relevance of the competencies developed during their graduate studies”.

Dimensions	Indicators	Items
Contents	Industry requirements	Are your graduate program’s subjects and contents aligned with the most recent developments in the industry?
Knowledge	Knowledge	How do you evaluate the knowledge acquired for each technological development?
Relevance of the competencies	Skills competencies	How relevant are the competencies acquired during studies for integrating into industry and innovation ecosystems? Do you consider that your graduate studies provided you with relevant and up-to-date information?
Knowledge updating	Perception of updating	Are your graduate program’s subjects and contents aligned with the most recent developments in the industry?
Preparation for technological changes	Preparation confidence	Do you have developed skills that will allow you to adapt to future changes? Do you trust your ability to face technological and innovation challenges in the industry after completing your graduate studies?
Preparation for innovation	Innovation	Do you consider that the competencies acquired during your graduate studies promote creativity and the ability to innovate in industrial contexts?
Interpersonal and collaboration skills	Teamwork	Do the competencies acquired enable you to collaborate effectively in interdisciplinary and team environments?

, respectively.

For data collection, a survey questionnaire was developed using Google Forms. The questions can be consulted in Appendix 1, hosted in the repository: https://github.com/cmejora/survey-cnn.git (accessed on 11 January 2025), which was applied electronically to a sample of 112 of 137 students from the last five semesters of the two master’s programs offered at the research center. The questionnaire consisted of 29 items based on the dimensions and indicators defined in the operational matrix of variables using Likert scaling. The sample size was determined using Equation (1) defined by [32], considering a confidence level of 95% and a margin of error of 3%, considering a population size of 137.

$$m={\displaystyle \frac{z^2{\sigma }^2\mathcal{N}}{c^2(\mathcal{N}-1)+z^2{\sigma }^2}}$$

(1)

where $z=$ confidence level of $95\%=1.96$; $c=$ error margin $=3\%$; $\sigma =$ standard deviation, when if not known, the constant uses a value of 0.5; and $\mathcal{N}$ represents the population size $=137$.

Once the data were collected, we applied preparation and cleaning processes to transform the data into an Excel spreadsheet in *.csv format to process the dataset using Python 3.12.4 and Pandas 2.2.2.

Regarding the data statistical analysis, we applied the Anderson–Darling goodness-of-fit test [33] (see Equation (2)) to determine the type of statistical test and assess whether the dataset fits with a normal probability distribution.

$$A_n^2=-n-\sum _{i=1}^n{\displaystyle \frac{(2i-1)}{n}}\left(ln\left(z_i\right)-ln\left(1-z_{n-i+1}\right)\right.$$

(2)

Moreover, Equations (3)–(5) are used to calculate the basic parameters for the Anderson–Darling test.

$$z_i={\displaystyle \frac{x_i-\mu }{\sigma }}$$

(3)

$$\mu ={\displaystyle \frac{1}{n}}\sum _{i=1}^n{x}_i$$

(4)

$$\sigma =\sqrt[2]{{\displaystyle \frac{\sum {\left(x_i-\mu \right)}^2}{n}}}$$

(5)

Thus, we formulated the following hypotheses to assess the experiment.

$H_0:x_i\approx N\left(\mu ,{\sigma }^2\right)$, the data are normally distributed.

$H_1:x_i\ne N\left(\mu ,{\sigma }^2\right)$, the data are not normally distributed.

The test was applied to each of the 29 sample variables in the dataset, and the p-value, which represents the true significance of the coefficient $A_n^2$, was also calculated applying Equation (6), proposed by [34]. Moreover, the p-value was computed considering the thresholds defined in

Table 4. Thresholds to compute the p-value.

If	p-Value
$13>A_n^{\prime 2}>0.600$	$e^{1.2937-5.709A_n^{\prime 2}+0.0186{\left(A_n^{\prime 2}\right)}^2}$
$0.600>A_n^{\prime 2}>0.340$	$e^{0.9177-4.279A_n^{\prime 2}-1.38{\left(A_n^{\prime 2}\right)}^2}$
$0.340>A_n^{\prime 2}>0.200$	$1-e^{-8.318+42.796A_n^{\prime 2}-59.938{\left(A_n^{\prime 2}\right)}^2}$
$A_n^{\prime 2}<0.200$	$1-e^{-13.346+101.14A_n^{\prime 2}-223.73{\left(A_n^{\prime 2}\right)}^2}$

.

$$A_n^{\prime 2}=A_n^2+\left(1+{\displaystyle \frac{0.75}{n}}+{\displaystyle \frac{2.25}{n^2}}\right)$$

(6)

It was found that in all cases $A_n^2<0.751$, the p-value $>0.1$; thus, the significance of 0.751 corresponds to the value from the Anderson–Darling tables for the normal distribution with a $95\%$ confidence level and a significance level of 0.05.

Therefore, we conclude that there is sufficient evidence not to reject the null hypothesis $\left(H_0\right)$, so the values of all 29 sample variables behave like normal distributions. According to the above, we applied parametric statistical tests such as the Pearson correlation and the t-test to analyze the sample variable.

Initially, the items were grouped based on relevant variables, such as “Industry 4.0 Knowledge”, which encompasses knowledge of information and communication technologies required by Industry 4.0. For example, to obtain the variable “IT Industry 4.0 Knowledge”, the following steps were taken:

1.

Select questions 15 to 22 (see Appendix 1, hosted in https://github.com/cmejora/survey-cnn.git (accessed on 11 January 2025), which correspond to the knowledge acquired on information technologies such as artificial intelligence, big data, cybersecurity, software development and programming, networks and communications, robotics and automation, IoT, and cloud computing. These topics align with the study programs offered at the research center.

2.

Based on an excerpt from Figure 1, we selected a subset of the results corresponding to the mentioned technologies.

Table 5. Subset with information technologies selected from Figure 1.

Technology/Sector	AGRI	AUTO	CON	DIGICIT	EDU	ENG	FS	GOV	HE	MANF	MIM	OILG	PS	TRANS
Artificial Intelligence	62	76	73	95	76	81	90	65	89	71	76	71	76	88
Big data	86	88	91	95	95	76	91	85	89	81	90	86	86	94
Cybersecurity	47	88	85	95	86	88	95	95	84	72	83	71	78	75
IoT	88	82	94	92	62	94	88	79	95	84	90	93	74	76
Robots	54	60	52	61	59	65	53	50	56	79	90	79	35	69
Cloud computing	75	80	82	95	95	88	98	95	84	92	87	86	88	94
Software and programming	80	82	92	95	95	88	98	95	89	80	80	83	80	92
Networks and communications	80	82	92	92	95	88	98	95	89	80	80	83	80	92

describes the obtained subset.

3.

The adoption percentages of the mentioned information technologies by the industrial sectors of the 26 countries were taken and normalized to be used as coefficients for the weighted sum. Thus, we developed Equations (7) and (8) to conduct these tasks.

$${sum}_j=\sum _{i=0}^n{p}_i$$

(7)

$$n_{ij=}{\displaystyle \frac{p_i}{{sum}_j}};\{i=1,nit\mid j=1,nsec\}$$

(8)

where $nit$ defines the total number of considered information technologies; the value is 8. In addition, $nsec$ represents the total number of included industrial sectors; in this case, the value is 14. Thus, $p_{ij}$ is the adoption percentage value of $I{T}_i$ in sector _j, and $n_{ij}$ is the adoption percentage normalized value of $I{T}_i$ in sector _j. Moreover, $su{m}_j$ is the sum of the adoption percentages of all $I{T}_i$ in sector _j.

4.

Applying Equations (7) and (8), we obtained

Table 6. Normalized values of the percentages of information technologies adopted in different sectors.

Technology/Sector	AGRI	AUTO	CON	DIGICIT	EDU	ENG	FS	GOV	HE	MANF	MIM	OILG	PS	TRANS
Artificial intelligence	0.1084	0.1191	0.1104	0.1319	0.1146	0.1213	0.1266	0.0986	0.1319	0.1111	0.1124	0.1089	0.1273	0.1294
Big data	0.1503	0.1379	0.1377	0.1319	0.1433	0.1138	0.128	0.129	0.1319	0.1268	0.1331	0.1319	0.1441	0.1382
Cybersecurity	0.0822	0.1379	0.1286	0.1319	0.1297	0.1317	0.1336	0.1442	0.1244	0.1127	0.1228	0.1089	0.1307	0.1103
IoT	0.1538	0.1285	0.1422	0.1278	0.0935	0.1407	0.1238	0.1199	0.1407	0.1315	0.1331	0.1426	0.124	0.1118
Robots	0.0944	0.094	0.0787	0.0847	0.089	0.0973	0.0745	0.0759	0.083	0.1236	0.1331	0.1212	0.0586	0.1015
Cloud computing	0.1311	0.1254	0.1241	0.1319	0.1433	0.1317	0.1378	0.1442	0.1244	0.144	0.1287	0.1319	0.1474	0.1382
Software and programming	0.1399	0.1285	0.1392	0.1319	0.1433	0.1317	0.1378	0.1442	0.1319	0.1252	0.1183	0.1273	0.134	0.1353
Networks and communications	0.1399	0.1285	0.1392	0.1278	0.1433	0.1317	0.1378	0.1442	0.1319	0.1252	0.1183	0.1273	0.134	0.1353

. Moreover, we formulated Equation (9) to obtain the normalized coefficients of all sectors for each information technology, and the results are depicted in

Table 7. Adoption percentages of IT across all industrial sectors and their normalized values.

Technology Adopted by All Sectors	(%)	${\mathit{c}}_{\mathit{i}}$
Artificial intelligence	78	0.118000087
Big data	88	0.134133369
Cybersecurity	82	0.1235431
IoT	85	0.129566304
Robots	62	0.093538791
Cloud computing	89	0.134584611
Software and programming	88	0.133465678
Networks and communications	88	0.133168059

.

$$c_i={\displaystyle \frac{{\sum }_j^{nsec}{n}_{ij}}{nsec}},\{i=1,nit\},$$

(9)

where $c_i$ represents the coefficients of information technology of all industrial sectors.

5.

The normalized values $c_i$ represent the coefficients to obtain the new variable “IT Industry 4.0 Knowledge”, based on the weighted sum of the data received from the responses to questions 15 to 22 of the survey questionnaire, according to Equation (10), defined to accomplish this objective.

$$k=\sum _{i=1}^{nti}{c}_i{p}_j;{\left\{p_j\right\}}_{j=15}^{22}$$

(10)

Here, k is the new variable that represents the knowledge acquired about the IT required by Industry 4.0 (IT Industry 4.0 Knowledge) and, $c_i$ are the coefficients of the normalized adoption percentages for each IT across all the considered industrial sectors. $p_j$ is the data resulting from the answers to questions 15 to 22.

Subsequently, descriptive data analysis was performed using Python 3.12.4 and the Python SciPy package, obtaining frequency distributions, measures of central tendency (mean, median, and mode), variability measures such as range, standard deviation, variance as well as cross-tabulation tables, and the corresponding frequency distribution graphs.

Finally, inferential analysis was performed, obtaining key correlation coefficients and applying the t-test to validate the research hypothesis. We used correlation coefficients to determine whether there is a relationship between two variables. They were obtained using Pearson’s correlation, defined by Equation (11) [35]. On the other hand, Equation (12) [35] demonstrates another way to calculate Pearson’s correlation.

$$r_{xy}={\displaystyle \frac{n\sum x_i{y}_i-\sum x_i\sum y_i}{\sqrt{n\sum x_i^2-{\left(\sum x_i\right)}^2}\sqrt{n\sum y_i^2-{\left(\sum y_i\right)}^2}}}$$

(11)

$$r={\displaystyle \frac{\sum \left(x_i-\overline{x}\right)\left(y_i-\overline{y}\right)}{\sqrt{\sum {\left(x_i-\overline{x}\right)}^2{\left(y_i-\overline{y}\right)}^2}}}$$

(12)

Thus, the Pearson correlation coefficient interpretation is based on Cohen’s suggestions [35], depicted in

Table 8. Interpretation of the Pearson correlation coefficient according to Cohen’s suggestions.

$0.00\le \left|r_{xy}\right|<0.10$	No correlation
$0.10\le \left|r_{xy}\right|<0.30$	Weak correlation
$0.30\le \left|r_{xy}\right|<0.50$	Moderate correlation
$0.50\le \left|r_{xy}\right|<1.00$	Strong correlation

.

Additionally, we performed a one-tailed t-test on relevant sample variables to evaluate their behavior concerning the mean and understand the trend of the responses about the Likert scale used.

In this research study, we applied a one-tailed test starting from the null hypothesis about the values of our sample variables in order to test whether they are equal to or greater than a proposed value on the Likert scale used for each sample variable, the questionnaire questions, to measure how much agreement or disagreement is expressed in them.

4. Experimental Results

In this section, we present the findings based on the statistical tests proposed in Section 3 (see Figure 2) on the dataset resulting from responses to the survey questionnaire applied to a sample of students from both programs concerning how students at a Computing Research Center perceive the relevance of the competencies and skills acquired during their studies in master’s programs to face the challenges of Industry 4.0 about the disruptive information technologies adopted.

We performed the Anderson–Darling normality test on all sample variables to assess the dataset’s normal distribution behavior. The tests demonstrated that all data of the sample variables behave as a normal distribution, which helped us to determine that the statistical tests to be applied are parametric.

Subsequently, we used descriptive statistics tests for all sample variables, computing measures of central tendency to analyze the behavior of all variables. The detailed values are shown in Appendices 2 and 3, hosted in https://github.com/cmejora/survey-cnn.git (accessed on 11 January 2025). Furthermore, we conducted the inferential statistical analysis, performing Pearson correlation and t-test for the mean. Below, we show and discuss the most important findings of the statistical tests on some of the variables of interest.

The percentage of each answer to all questions of the survey is presented in Figure 3.

Most students enrolled in the Master’s in Computer Science Program refer to the male gender. This finding means that the programs should encourage gender equality and empower women in STEM because it is a fundamental human right and a necessary foundation for a peaceful, prosperous, and sustainable world [36].

Around 21% of students considered the competencies acquired during their graduate studies relevant, 40% neither relevant nor irrelevant, and 45% irrelevant. This finding reveals that approximately 85% of students could not ensure the acquired competencies were enough to tackle real issues in Industry 4.0. Thus, most students think that the subjects and content of their graduate study programs are misaligned with the latest industry developments. Moreover, approximately 36% of students considered that the information technology knowledge acquired during their studies is sufficient or adequate, while 22% think that it is insufficient (KR row in Figure 3). The mean, median, and mode values indicate that more than $60\%$ of the students believe that the knowledge they acquired about information technologies does not satisfy the essential requirements of Industry 4.0, or they have a neutral position regarding this topic.

According to the results obtained on how students consider the knowledge acquired about the information technologies that Industry 4.0 has adopted, we can observe, as shown in Figure 4, that there are four information technology fields where they perceived that the knowledge acquired is insufficient, which represent half of the required IT.

4.1. Cross-Tabulation Tests

On the other hand, to analyze the relationship between the relevance of competences acquired and the alignment of these with the latest industry developments, we generated the corresponding cross-tabulation test, as shown in

Table 9. Cross-tabulation test: “How relevant do you consider the competencies acquired during your graduate studies for your integration into industry and innovation ecosystems”? vs. “Do you consider the subjects and content of your graduate studies to be aligned with the latest industry developments”?

		Do you consider that the subjects and content of your graduate studies are aligned with the latest industry developments?
	Assessment Values	Highly misaligned	Misaligned	Neither aligned nor misaligned	Aligned	Total
How relevant do you consider the competencies acquired during your graduate studies for your integration into industry and innovation ecosystems?	Irrelevant	13	34	3	0	50
	Neither relevant nor irrelevant	0	24	10	4	38
	Relevant	0	1	1	22	24
	Total	13	59	14	26	112

. The results of this test reveal that students’ perception is that the knowledge acquired is not aligned with or does not have sufficient relevance to meet the requirements of Industry 4.0.

Similarly, we examined the relationship between other variables of interest, such as “IT Industry 4.0 Knowledge”, and the perceived relevance of competencies acquired during graduate studies for integration into industry and innovation ecosystems. So, we created its cross-tabulation test shown in

Table 10. Cross-tabulation test: Industry 4.0 knowledge vs. “How relevant do you consider the competencies acquired during your graduate studies for your integration into industry and innovation ecosystems”?

		How relevant do you consider the competencies acquired during your graduate studies for your integration into industry and innovation ecosystems?
	Assessment Values	Irrelevant	Neither relevant nor irrelevant	Relevant	Total
Industry 4.0 knowledge	Insufficient	25	0	0	25
	Neither sufficient nor insufficient	25	22	0	47
	Sufficient	0	16	21	37
	Entirely sufficient	0	0	3	3
	Total	50	38	24	112

. The results suggest that the students’ perception is that the knowledge acquired about IT adopted by Industry 4.0 is insufficient to face the challenges of the industry and innovation ecosystems.

4.2. Inferential Analysis

Regarding inferential statistical analysis, to reinforce the descriptive analysis on the cross-tabulation test presented in Table 9, the Pearson correlation (see Equation (12)) was applied to analyze how close the affinity between these questions is. The value obtained using the Pearson correlation coefficient analysis was $r=0.7932$. According to Table 8, which describes Cohen’s suggestions, this value reflects a strong positive correlation between both variables. So, by having a high value of r, we inferred that both variables are strongly associated. It reaffirms the interpretation that students perceived that the knowledge acquired during their graduate studies is not fully aligned with the latest developments in the industry.

Moreover, when evaluating the relationship between the answers to the relevance of the acquired competencies and the knowledge of Industry 4.0 as presented in Table 10, we obtained a Pearson correlation coefficient value of $r=0.8081$; as in the previous case, it implies a strong positive correlation of the two variables, which reinforces the interpretation that students perceived that the knowledge acquired about IT adopted by Industry 4.0 is not sufficient for their integration into the innovation and industry ecosystems.

4.3. The t-Test

Considering the hypothesis test and understanding students’ perception of the knowledge acquired in information technologies adopted by Industry 4.0, we applied the one-tailed t-test for a single sample mean to the variable “IT Knowledge Industry 4.0” as follows:

• The variable “IT Knowledge Industry 4.0” can take the values according to the Likert scale shown in

  Table 11. Values of the Likert scale for the “IT Knowledge Industry 4.0” variable.

  IT Knowledge Industry 4.0
  1	Totally insufficient
  2	Insufficient
  3	Neither sufficient nor insufficient
  4	Sufficient
  5	Totally sufficient

  .
• Students are expected to consider the knowledge acquired during their studies on the IT adopted by Industry 4.0 as sufficient or totally sufficient.
• The observed mean of the data obtained from the sample is $3.36$.
• Is there evidence for students to believe otherwise?
• $H_0:\mu \ge 4$ vs. $Ha:$$\mu <4$.
• The statistic test: $t=\sqrt{n}{\displaystyle \frac{(\overline{x}-\mu )}{s}}$.
• Thus, one sample t-test considers the following values:

  –

  $n=122$.

  –

  $\overline{x}=3.16071429$.

  –

  $s=0.80037797$.

• Then, the confidence interval is $95\%$, with a significance level of $\propto =0.05$.
• t = −11.097463, which was computed from the dataset.

The critical value $\propto =0.05$, $z_{0.05}=-1.6448536$ is the threshold that separates the rejection region from the non-reject considering $H_0:\mu \ge 4$.

Since $t<z_{0.05}(-11.097463<-1.6448536)$, therefore, there is sufficient evidence to reject the null $H_0:\mu \ge 4$ hypothesis, and it implies that students perceived that the knowledge acquired during master’s science studies on IT necessary to join Industry 4.0 is not sufficient.

Moreover, we computed the confusion matrix presented in

Table 12. Confusion matrix obtained for this test.

	Real ${\mathit{H}}_0:\mathit{\mu }\ge$ 4 True	Real ${\mathit{H}}_0:\mathit{\mu }\ge$ 4 False
Reject $H_0:\mu \ge 4$	Type I Error (FP)	True Positive (TP)
Non-reject $Ho:\mu \ge 4$	True Negative (TN)	Type II Error (FN)

to visualize the four possible results of this test in terms of type I and type II errors.

For this case, the statements are as follows:

• Type I error (FP): If $H_0:\mu \ge 4$ is true and we reject it, then we make a type I error. The probability of this error is $5\%$, which implies a significance level.
• Type II error (FN): If $H_0:\mu \ge 4$ is false and we accept it, then we commit a type II error. It does not happen in this case since we rejected $Ho:\mu \ge 4$.

Since the observed value of $t(-11.0977463)$ is much smaller than the critical value (− 1.6448536), we reject $Ho:\mu \ge 4$. Thus, if $Ho:\mu \ge 4$ is false, a true positive (TP) value is obtained.

Given the too-small value of $t(-11.0977463)$ compared with the critical value, it is very unlikely that this result is a type I error. This value supports the decision to reject $H_o:\mu \ge 4$ with high confidence, minimizing the probability of type I or II errors. It is worth mentioning that this test was also applied to other interest variables, obtaining similar results.

5. Discussion

The finding that approximately 85% of students perceive that their acquired skills are insufficient and that the majority consider the curriculum content to be misaligned with the industry underscores a critical disparity between academic training and labor market demands in the Industry 4.0 paradigm. The study by Low et al. (2021) [8] found a significant gap in soft skills, suggesting that Industry 4.0 readiness requires not only aligned technical skills but also transversal skills, something that we also mention in our study, which complements our findings. Both studies agree that educational institutions need to adapt their curricula and collaborate with industry to close the skills gap.

On the other hand, the perceived inadequacy of knowledge in half of the key information technologies for Industry 4.0 is a crucial finding. It suggests the need to review academic programs to align their content with current industry requirements, especially in areas where the perception of inadequacy is high, as indicated in the results (although specific areas such as cloud computing and IoT are mentioned as examples of high inadequacy in Section 6). This study highlights a disparity where students feel prepared in traditional areas (such as programming) but perceive deficiencies in emerging technologies linked to automation and sustainability.

The strong positive correlations ($r=0.7932$ and $r=0.8081$) demonstrate a strong association between students’ perceptions of the relevance of acquired skills and the alignment of curriculum content with industry and their knowledge level about Industry 4.0 IT. It means that although students with higher knowledge of Industry 4.0 IT tend to perceive better alignment, the general perception remains that not all content is fully aligned with the knowledge required by the industry.

The result of the t-test ($t=-11.097463$), being significantly lower than the critical value, provides strong evidence to reject the null hypothesis ($H_0$: knowledge is sufficient or greater) and, therefore, accept the research hypothesis (established in Section 3.2 and confirmed in Section 4), which postulates that students perceive that the knowledge acquired through master’s programs is insufficient for their successful integration into innovation and industry ecosystems. This result is highly reliable given the magnitude of the t statistic.

The findings reinforce the argument that a lack of curricular updating directly affects the perception of Industry 4.0 readiness and underscore the need for universities to collaborate with the industrial sector to adapt their programs. The perception of curricular misalignment aligns with the challenges identified by Deák and Kumar (2024) [7] for the digital training of educators. The systematic review found barriers such as a lack of technological infrastructure, the need for teacher training, required pedagogical changes, and time constraints. These systemic challenges in educational institutions may cause the misalignment that students perceive in our study. Halili and Sulaiman (2021) [29] also pointed to the need for higher education institutions to change the framework of their academic programs to remain relevant and meet the demands of Industry 4.0, reinforcing our findings on the need to update academic programs.

Finally, this study contributes to the global discussion on sustainable education systems (ODS 4), demonstrating that the perception of competencies is mediated by integrating ethical and environmental principles into curricula. The findings suggest that in addition to technical skills, transversal skills and competencies such as critical thinking and social responsibility need to be included in postgraduate programs to comprehensively address the challenges of Industry 4.0, balancing technological progress with environmental protection and social equity. Up-to-date technical knowledge is a fundamental predictor of employability and sustainable innovation (SDG 9). We agree with the study by Kioupi and Voulvoulis (2019) [2], who present a systemic and conceptual framework to redefine Education for Sustainable Development (ESD) in a broad sense, seeking to connect the ODS with learning outcomes and transform society, addressing the ambiguity and lack of perceived effectiveness of current ESD practices.

6. Conclusions and Future Work

The growing demand for highly skilled professionals in the emerging digital technologies of Industry 4.0 poses a significant challenge for higher education institutions (HEIs). In this context, HEIs worldwide must adapt curricula and academic programs to meet these new demands, prepare their students for successful integration into innovation and industry ecosystems, and address global challenges from a sustainability perspective.

The conclusions of this study, based solely on the perceptions of students in postgraduate computer science programs taught by the Centro de Investigación en Computación (CIC) of the IPN, revealed a significant disparity between the academic training received and the current and future demands of Industry 4.0.

The main findings focus on the perception of insufficient knowledge and skills. Most students (approximately 85%) do not consider the skills and knowledge acquired in their master’s programs sufficient or adequate to address the challenges of Industry 4.0. Only 33% of students perceived their knowledge as enough. Another finding relates to curricular misalignment. Many students (64.3%) perceived that the content of their graduate programs was misaligned or severely misaligned with the latest industry developments. A strong positive correlation exists between the perception of competency relevance and curricular alignment with industry.

Furthermore, while students may feel prepared in traditional areas, they perceive shortcomings in key emerging Industry 4.0 technologies such as cloud computing and IoT, as well as those linked to automation and sustainability, particularly robotics for energy efficiency, implying a need to update academic programs to consider emerging technologies. This study revealed the importance of transversal, sustainability, and technical skills. Therefore, it acknowledged the demand to integrate transversal skills such as critical thinking, social responsibility, teamwork, and ethics. Thus, academic programs must align with the United Nations Sustainable Development Goals (SDGs 4, 9, and 12) by 2030 to develop professionals committed to sustainable innovation.

Summing up, the results highlight that students perceive a significant gap between the skills acquired and the requirements of Industry 4.0. Moreover, the t-test confirmed that the perception of sufficiency in technological knowledge (mean = 3.36) is significantly lower than the expected value ($\mu \ge 4,p<0.05$).

Finally, this study accepts the hypothesis that students perceive the knowledge acquired through master’s programs as insufficient compared to the requirements for successful integration into the innovation and industrial ecosystems of Industry 4.0. Ultimately, this study concludes that a critical gap exists between the training offered in the evaluated graduate programs and the skills demanded by Industry 4.0, which requires curricular updating, greater collaboration between universities and industry, and the integration of sustainability and transversal skills to prepare future professionals adequately.

Future work is oriented towards adopting a more comprehensive approach, expanding the sample to include representatives from the industrial, educational, and government sectors and faculty to complement the perceptions of computer science students analyzed in this study. It also suggested that socioeconomic variables be incorporated into the analysis, considering data related to online education. The results could help improve master’s programs, facilitating job placement in innovative and sustainable industries, bridging the academic–industry gap, and fostering responsible innovation.