The Future of Higher Education: Trends, Challenges and Opportunities in AI-Driven Lifelong Learning in Peru

Abstract

This study analyses future trends in lifelong learning in the Peruvian context. Using the DeflyCompass model, an artificial intelligence tool, the main trends affecting the evolution of postgraduate studies were identified, including the impact of generative AI on the personalisation of education, the transformation of work and the growth of Generation Z as key players in the educational environment. The methodology applied combines a mixed qualitative and quantitative approach, based on the opinion of experts—45 participants from seven public/private universities in Peru—the technique of semantic projections, the use of generalist search engines and specialised databases, and other digital management resources such as Google Scholar profile analysis and online marketing campaign design tools. In particular, a total of 150 scientific papers and 300 articles from generalist sources were analysed. This approach made it possible to select, analyse and quantify the main trends in higher education in Peru and to assess their potential impact on the future development of graduate schools, specifically in the case of the Pontificia Universidad Católica de Perú (PUCP). The results highlight the importance of adapting postgraduate studies to new demands, such as the adoption of generative AI, the adaptation of personalised education and the integration of digital technologies to enhance the personal and professional growth of students. It also highlights the need to incorporate strategies that address the transformation of work, with a focus on developing digital skills and preparing for an ever-changing work environment. The study thus provides a guide for Peruvian universities on how to adapt their graduate programmes to emerging trends, promoting a flexible and technologically advanced education that responds to the needs of future professionals.

1. Introduction

Lifelong learning is facing major challenges in a rapidly evolving global context due to digitalisation, the development of artificial intelligence, the transformation of work and demographic changes [1] In this scenario, universities seek to remain at the forefront by adapting to emerging trends and preparing future professionals for an increasingly complex and dynamic environment [2,3,4]. Various studies have highlighted the importance of understanding emerging trends in postgraduate education in order to adapt training programmes to the needs of the 21st century [5]. In its report on the future of education, UNESCO highlights the need for a new social contract for education based on the principles of inclusion, equity and social justice. According to this institution, incorporating futuristic trends into the study of education is crucial to meet the challenges of an increasingly uncertain and complex world [4].

These trends enable education systems not only to adapt to technological and economic changes, but also to serve as catalysts for social change, promoting equity and sustainable development [6]. It is emphasised that education must be a common good, capable of fostering sustainable development and social cohesion in an increasingly uncertain and complex world [7]. Knowledge of global trends is essential to ensure that educational institutions can offer programmes that not only meet local needs but are also internationally competitive [3]. This means anticipating changes in the global labour market, such as the growing demand for digital and transversal skills [2,8,9,10].

In the area of postgraduate education, the trends observed enable the alignment of curricula with international standards, facilitate academic and professional mobility and ensure that students have the necessary skills to participate in an increasingly interconnected economy [10,11,12]. The literature also highlights the need for universities to anticipate technological changes and student expectations, and to implement pedagogical innovations that improve the quality and relevance of graduate programmes [2,13]. According to data from the Peruvian higher education market, the number of postgraduate students has increased by 30% in the last decade, driven by the growing demand for professional training in an increasingly competitive work environment [14]. This expansion also reflects the need for education to become a public project and a common good, to ensure that higher education is accessible to all and contributes to a more just and equitable future [15,16]. However, there is still a significant gap in the quality and accessibility of graduate programmes, which poses a challenge for universities seeking to provide quality education in line with market needs [13].

Various studies have identified several key trends for the future of education, including the need to promote education as a common good; to promote lifelong learning, especially in postgraduate education, which should be seen as a crucial stage in lifelong learning; to adopt cooperative and solidarity-based pedagogies; and to develop competencies for sustainability [17,18,19]. Lifelong learning is essential to maintain the relevance of professionals in a dynamic labour market and to promote active and responsible citizenship [20,21]. These trends are in line with the objectives of most Peruvian universities, which are to anticipate the demands of the labour market and the expectations of students, and to guarantee training that contributes to sustainable development and social equity [14,22,23]. This study is framed by the need to understand these futurisation trends and how they can influence educational provision. Futures trends refer to long-term changes and projections that affect both the structure and content of graduate programmes [24].

In the case of Peru, it is crucial to anticipate the demands of the labour market and the expectations of students, integrating the personalisation of learning, the adoption of new technologies and the development of digital skills. Generation Z, an increasingly influential group in the field of education, presents new challenges and opportunities to redefine pedagogical strategies and teaching methods [25]. Continuous training and lifelong learning are essential to ensure that professionals can update their skills and adapt to new market and societal demands [10,11].

This is particularly relevant for postgraduate education, where programmes should provide flexible and adaptable learning opportunities that enable students to remain competitive in a constantly evolving work environment. This study aims to analyse and understand these trends using advanced artificial intelligence tools, content analysis and semantic projections. By combining qualitative and quantitative methods, it aims to provide a strategic guide for universities to proactively respond to the challenges of a changing environment and ensure relevant, innovative and forward-looking postgraduate education. Specifically, the objectives of this document are as follows.

• To identify the key challenges that Peruvian universities must address in the coming years concerning the future of education.
• This analysis aligns with global educational trends while considering specific factors that could influence the strategic goals of universities in Peru.
• To present an analytical methodology that integrates conceptual analysis, natural language processing and quantitative techniques based on semantic projections, enabling a more in-depth approach to foresight for strategic planning.

2. Methodology

The methodology used in this study combines a qualitative and quantitative mixed approach, with the aim of identifying and analysing futuristic trends affecting graduate education in Peru [26]. Advanced artificial intelligence tools (such as generative AI, natural language processing utilities, word embeddings), text analytic tools (e.g., Voyant 4.0, NotebookLM 2024) and related instruments (like Semrush), along with semantic projections, were used to ensure a comprehensive understanding of emerging trends and their relevance to educational offerings. The purpose of using these tools is to process information gathered from scientific articles, technical reports, open searches using general search engines like Google, and other sources, in order to synthesize the data that will be useful for the subsequent steps of our analytical method. While the other AI tools mentioned are well-known and widely used in scientific research, semantic projections require a more detailed explanation, as their application within this framework is less common. A semantic projection is an index that provides a sharing coefficient between multiple terms, typically a real number between 0 and 1, which represents the degree to which two semantic terms (such as words, short phrases or tokens) share semantic environments. A typical example would be, given a term (e.g., “gold”) and a semantic environment represented by a set of related words (referred to as the “universe”), such as metals: silver, zinc, etc., we fix a specific repository of scientific papers (e.g., arXiv) and compute the number of documents in which two terms (e.g., “gold” and “silver”) appear together, divided by the number of documents where the first term (gold) appears alone. The central idea of this method is to measure the “sharing rate” of two selected terms within a corpus of documents. This is understandable when we combine terms linked to established trends, such as “Environmental sustainability”, which are widely accepted and present in our trend database, with other terms referring to specific aspects of the analysis target, such as “Student motivation”. If that ratio, normalized by the specific search term (“Student motivation”), is large, it follows that this search term shares the positive nature of the trend we have assumed in our catalogue of well-established trends (in this case, “Environmental sustainability”). Note that this sharing rate can be accurately calculated once the measurement framework is established, such as using the documents in arXiv, or open searches with the Google engine.

The specific procedure we employ is based on what we call the Defly Compass framework, which involves determining a list of trends [27] that is periodically updated and categorized into several groups, following an adapted version of the ASPECT scheme (Arts, Science, Population, Economy, Culture, Technology). Using various methods, which will be explained below in relation to the research reported here, we identify the main trends concerning the six fields in the standard ASPECT scheme [28]. Subsequently, a process involving natural language processing and other AI tools determines and quantifies the key trends relevant to the development of higher education in Peru, which is the focus of this study.

As mentioned above, the initial study and research design were carried out in the specific context of the PUCP, with other universities being considered in a second phase. Although the approach could be considered a case study, we believe that the partial and overall conclusions can be perfectly extrapolated to the entire Peruvian university context.

2.1. Participants

The analysis involved a group of 45 experts, including planning directors, graduate deans and vice-rectors from the Peruvian higher education system (specifically, from 7 public/private universities). This group, referred to as the seed group and represented in the study as Group 0, provided valuable insights into the key trends in postgraduate education. These experts were chosen based on their extensive academic experience and expertise in educational innovation and strategic development.

2.2. Data Collection

The first step involved an initial selection of trends from the list provided by the Defly Compass model. The members of Group 0 individually evaluated which trends were most relevant for the study of higher education in Peru by assigning scores to each trend, as will be explained in the next section.

Then, an extensive documentary approach was employed for data collection, analyzing approximately 150 academic documents and 300 articles from general sources related to higher education and emerging trends, reviewing their content and obtaining keywords and concepts through automatic language analysis using tools such as Voyant and specific R programs for natural language processing. This number of documents was considered enough to identify the main search topics and terms needed for the next steps of the analysis. They were sourced using both general and specialized search engines, including Google Scholar, Scopus, Web of Science, DOAJ, arXiv, ResearchGate and others, to provide a comprehensive overview of academic and scientific trends. Also, the selected experts prioritized key reports for strategic decision-making. For instance, essential reports on higher education, such as “The Future of Higher Education” (2024) and the “Educause Horizon Report” (2023), were chosen. The search of documents focused on terms directly related to trends in higher education, strategic planning, the Peruvian university system and similar areas.

To define the search terms, we conducted an initial study of digital marketing campaigns. Approximately 200 specific samples of online marketing campaigns from Peruvian universities were gathered, which included analysis of user behaviour, ad performance, and search trends related to higher education in Peru. This analysis was carried out using digital marketing tools such as Google Ads and the Semrush platform. The insights gained from this data helped better account for the preferences of potential graduate students and market demands. This information was used to define the analytical terms for later stages of the study, where semantic projections were applied to identify the sharing rates between emerging trends and specific terms related to the Peruvian higher education context. Thus, the result of the data collection process was the determination of a set of relevant topics concerning futures of higher education in Peru, as well as a related list of concepts/terms describing them.

2.3. Data Analysis

Data analysis was carried out in two main phases, using advanced artificial intelligence techniques (word embeddings) to measure semantic proximity between concepts and to identify key relationships between emerging trends. In the qualitative phase, content analysis and thematic analysis were used to identify patterns and trends in the collected data, with ChatGPT 4o and Voyant 4.0 used to extract key information from the documents and provide a detailed view of emerging themes. The use of ChatGPT, supported by recent studies [29,30,31], proved effective in identifying emerging themes and producing coherent summaries. The quantitative phase involved an analysis of the text using semantic projections and data mining [9]. This phase included the use of tools such as Google Trends, Google Ads and Semrush, which provided data on the popularity and evolution of key terms in the field of Peruvian graduate education. Search trends were also analysed to correlate the relevance of emerging trends with interest and demand in the field of education. Specifically, semantic projections were used to analyse the relationships between the key terms identified and their future impact on graduate education in Peru. Key trends identified include the increasing demand for flexible learning modalities, the expansion of micro-credentials and the growing importance of artificial intelligence, as well as the dissolution of the dichotomy between online and face-to-face teaching.

2.4. Validation of Results

The final results were presented to a group of experts (including some members of Group 0), who discussed the validity of the findings. Due to the nature of the research, immediate validation is not possible, as the results represent future trends that cannot be verified at present.

3. Results

The study on emerging trends reveals key dynamics to transform the higher education sector in Peru, through an in-depth analysis of the main global trends combined with artificial intelligence tools together with the knowledge of the experts of Group 0. Let us describe the results obtained at each step.

Step 1. Initial selection of trends. The analytical process began with the evaluation of 276 global trends [9], which were reduced to 69 through an initial filtering process based on potential impact criteria. A second selection, based on the expertise of the members of Group 0, led to the identification of 30 macro trends expected to significantly impact higher education in Peru in the coming years. This process was carried out as follows. The experts rated each trend on a scale from 0 to 5. Then, the mean and standard deviation of the 45 scores assigned to each trend were calculated, and the selection of trends was made by applying a scoring threshold (mean greater than 4 and standard deviation less than 1). This criterion was used to identify the most relevant trends. This was the starting point of a process that, through an analysis of external factors, the use of digital marketing tools and strategic documents, along with the selected trends, led to the creation of future scenarios for lifelong learning in Peru, specifically for the case of PUCP.

Table 1. Selected trends.

1. Customisation	11. Staying in education longer	21. Business mash-ups
2. Personalisation	12. Direct democracy	22. Labor shortages
3. Individualisation	13. Population Aging	23. Hyperconnectivity
4. Empowerment	14. Gen Z Rising	24. Greater Interconnectedness
5. Multiculturalism	15. Customised learning	25. Applied Adaptive AI
6. Beyond Globalisation	16. Rising levels of education	26. Synthetic media
7. Digital Culture	17. Changing Business Volatility	27. AI risks for business
8. Digital Natives	18. Service Economy	28. Intelligent environments
9. New ways of Working	19. Knowledge-based economy	29. Technology Convergence
10. Reimagine work	20. Innovation as a key driver	30. Digital Self-actualisation

shows the results.

Table A1. Matrix displaying the scores of the first 15 experts from Group 0 for the list of selected trends provided in Table 1.

Trend	E1	E2	E3	E4	E5	E6	E7	E8	E9	E10	E11	E12	E13	E14	E15…
1	3	5	4	5	3	5	3	5	4	5	3	5	4	5	4
2	3	5	5	5	5	5	3	5	5	5	5	5	5	5	5
3	3	5	4	5	3	5	3	5	4	5	3	5	4	5	4
4	4	5	4	4	5	4	4	5	4	4	5	4	4	4	4
5	3	5	4	5	5	3	3	5	4	5	5	3	4	5	4
6	4	5	4	4	5	3	4	5	4	4	5	3	4	4	4
7	4	5	5	5	5	5	4	5	5	5	5	5	5	5	5
8	4	5	5	5	5	5	4	5	5	5	5	5	5	5	5
9	4	5	5	5	4	5	4	5	5	5	4	5	5	5	5
10	4	5	5	5	4	5	4	5	5	5	3	5	5	5	5
11	4	5	4	5	5	5	4	5	4	5	4	5	4	5	4
12	4	5	3	4	5	5	4	5	3	4	4	5	3	4	3
13	3	5	4	5	4	4	3	5	4	5	4	4	4	5	4
14	4	5	4	5	3	4	4	5	4	5	3	4	4	5	4
15	4	5	5	5	4	5	4	5	5	5	4	5	5	5	5
16	4	5	4	5	3	5	4	5	4	5	4	5	4	5	4
17	3	4	4	5	4	5	3	4	4	5	5	5	4	5	4
18	3	4	4	5	4	5	3	4	4	5	4	5	4	5	4
19	3	5	5	5	5	5	3	5	5	5	4	5	5	5	5
20	3	5	5	5	4	5	3	5	5	5	4	5	5	5	5
21	3	4	4	5	4	5	3	4	4	5	4	5	4	5	4
22	3	5	4	4	4	5	3	5	4	4	4	5	4	4	4
23	3	5	5	5	4	5	3	5	5	5	5	5	5	5	4
24	3	5	5	5	4	5	3	5	5	5	3	5	5	5	4
25	3	5	5	5	4	5	3	5	5	5	4	5	5	5	4
26	3	4	4	5	4	5	3	4	4	5	4	5	4	5	4
27	3	4	5	4	4	5	4	4	5	4	4	5	5	4	4
28	3	4	5	5	5	5	3	4	5	5	5	5	5	5	5
29	3	4	5	5	5	5	3	4	5	5	5	5	5	5	5
30	4	4	4	5	4	5	4	4	4	5	4	5	4	5	4

in Appendix A presents the submatrix of the first 15 scores for the 30 selected trends, listed in the same order as in the previous table. It shows that the scores in each row are relatively similar.

The similarity of the cosine, which does not take the average value of the vector but 0 as a reference, is very high in all cases, demonstrating how similar the assessments of the six evaluators are, although with differences. The average value of the cosine was 0.9960342 with respect to the mean. The selection of the 30 trends highlights aspects related to individuality, the social and economic changes of post-globalisation, and the influence of artificial intelligence expected in the coming years that will affect many aspects of life, and, specifically, in education. Also considered are foreseeable changes in the economy, increasingly based on the service sector, related to volatility, globalization and other aspects.

Step 2. Trend analysis using digital marketing tools. This section presents the evaluation of the most frequently searched terms related to higher education, master’s degrees and doctorates in Peru, with a focus on PUCP. Advanced digital marketing tools were used, primarily Semrush, which analyses user searches without relying solely on Google, and Google’s Google Ads tool. Both tools (Semrush and Google Ads) enable us to estimate the most relevant terms for assessing the potential impact of futurisation trends within the specific contexts we are interested in—in this case, higher education in Peru and at PUCP.

The results are sorted by the volume of searches (in units of volume of information, not in number of searches). We present only those results that give non-zero scores, although some examples of results that give practically no results are indicated to illustrate the type of analysis that has been done. It should be noted that what is quantified is what people are looking for when they search for information on the internet with the selected terms. In the framework of digital marketing, this is essential to be able to assign a price to each search term for the advertising of a certain product, which is the business of the usual search engines on the internet. It is important that Semrush works with concepts, and not only with the specific terms that are used, which gives more generality to the information obtained. Consistent with the approaches of other reports, we selected combinations of terms related to higher education and location (Figure A1 in Appendix A).

We consider searches with higher scores to be more relevant. The “Trends” column tracks the monthly evolution of searches over the past year; as shown, there are no significant increases, so this information is not considered particularly important. The reference search we used for the semantic projections, “Educación Superior Perú,” has a low search volume, which is expected, as it is too broad a phrase for most users to search for. However, “doctorado Perú” and “Maestría Perú” show a much stronger presence. Some of the terms associated with PUCP show similar or higher search volumes, highlighting the central role PUCP plays in higher education, particularly in master’s and doctoral programmes in Peru.

Below, we present an analysis to compare the results from Step 1 on trends. When we analyse the results with Semrush, we find the terms appearing in

Table 2. List of trends highlighted in the results of Step 1 with their relative scores. The averages of semantic projections based on open searches in Google, Google Scholar and some scientific repositories are shown.

Continuing Education	0.6	Labour innovation	0.45	Personalisation	0.35	Global education	0.2
Personal growth	0.55	Digital services	0.42	Digital culture	0.35	Citizen participation	0.18
Service Economy	0.55	Artificial Intelligent	0.4	Virtual Media	0.35	Artificial Intelligence	0.15
Knowledge Economy	0.5	Education Industry	0.35	Personalised Services	0.2	AI-Generated Content	0.15
Transformation	0.5	AI Risk Management	0.35	Online Self-Realisation	0.2	Gen Z	0.12

as related to the ones given in Step 1. The sharing rate of these terms, computed by means of semantic projections of these terms with the composed term “higher education Peru” in the same way that will be explained below, gives the numerical values appearing in the table. It should be noted that the information presented here does not reflect its relative presence on the internet, since internet users do not search for general topics or abstract descriptive terms, but for specific questions that they want to find in order to obtain specific information or a service. Therefore, it is not expected that the search concepts presented here coincide with the results of the quantification of the trend terms in Step 1, but that they complement the information obtained there.

In summary, the futurisation terms related to the transformation of work and education, the connections between education and the economy, the role of digital elements in education and citizen participation are reinforced. However, topics such as the environment and multiculturalism are notably absent from the 30 trends selected by Group 0, despite showing significant search activity in the context described in this section, as observed through the Semrush tool.

Step 3. Detection of main concepts by means of semantic projections. As outlined in Section 2, the semantic projection method involves identifying and quantifying the overlaps between selected search terms, which represent the key factors in the issues under study [32]. We employed the ASPECT analysis model [28], which categorizes general topics (Arts, Society, Education, etc.), a framework that will be explained in detail later. This method enables trends to be grouped by category, allowing for easier measurement of their relative importance. In our case, it helped clarify how macro-trends impact various aspects of educational development at PUCP. Below, we show the selected trends by each of the categories of ASPECT, together with the graphics that were used to select them (only the items with higher scores are presented in these figures).

(A) Arts, Lifestyle, Entertainment, Culture and Sport: Customisation, Personalisation, Individualisation, Digital self-actualisation, New ways of working, Reimagine work (Figure 1).

(S&P) Society, Politics, Ethics, People and Psyche: Empowerment, Direct democracy, Multiculturalism, Beyond globalisation, Population aging, Gen Z rising (Figure 2).

(E) Education: Staying in education longer, Customised learning, Rising levels of education around the world, Digital self-actualisation, Knowledge-based economy (Figure 3).

(C) Competitors, Market and Constraints: Beyond globalisation, Changing Business Volatility, Service Economy, Knowledge-based economy, Innovation as a key driver and competition factor, Business mash-up, Labour shortages, Business Security Risk Management with Artificial Intelligence, New ways of working, Reimagine work (Figure 4).

(T) Technologies, Sciences and Inventions: Digital culture pervading all daily life, Digital natives, Hyperconnectivity, Greater Interconnectedness, Applied Adaptive AI and Automation (even for creativity), Synthetic media, New Interface and Intelligent environments, Technology convergence, Digital self-actualisation (Figure 5).

From the numerical information provided by these figures for each trend, we will set the relevant trends that have to be taken into account for the design of scenarios in the final section.

Step 4. Reports for strategic decision-making. With the aim of completing the analysis with other relevant sources of information, we also analyse six documents on higher education selected by the group of experts using Delphi methodology [33,34], which focus both on the international context and on local aspects. The reports are the following: Global Citizenship Education for a More Just and Equitable World [16]; The Future of Higher Education [8]; Horizon Report: Teaching and Learning Edition [35]; Catalysing Education 4.0: Investing in the Future of Learning for a Human-Centric Recovery [13]; The Future of Higher Education in a Disruptive World [2]; Reimagining Our Futures Together: A New Social Contract for Education [4].

The objective is to provide a clear synthesis of the key ideas and highlight the most important issues identified. In the second part, the findings from this analysis are compared with the conclusions from earlier steps and specific elements that contrast with the trend analysis results. The goal is to align the conceptual insights and quantifications from our tool (presented in Step 3) with the most significant issues highlighted by experts in the six reports under review, emphasizing areas of agreement and divergence.

The aim is to detect which trends identified by Group 0 are reinforced by the six analysed reports. To achieve this, both the content of the texts and an automatic analysis of the six reports were combined using two complementary methodologies: generative AI and formal text analysis. The first methodology leverages generative AI, using tools like ChatGPT (alternatively NotebookLM) to analyse and synthesise the main ideas in each report. The second uses formal text analysis software (such as Voyant), which allows for counting term occurrences, identifying word correlations, and extracting various formal data from the texts. By integrating these analyses, comparing ideas, proposals and solutions on education issues, we identified the main descriptors and compared them with our previous findings.

The results can be summarised in the following topics to be understood in the context of higher education, which, in a sense, synthetise the main results: Artificial Intelligence (AI), Human Approach to Education, Lifelong Learning, Democratization of Knowledge, Educational Evolution, Teaching Flexibility, Emerging Technologies, Skills Development, Personalisation of Learning Digital Competencies, Innovation in Education, Global and Social Challenges, Change and Disruption, Demographic Shifts, Student Experience, Fostering the Innovation Ecosystem, Environmental and Social Sustainability, Research and Development, Funding Accessibility and Equity in Education, Remote and Collaborative Learning Approaches.

Table 3. Frequentist analysis of the main keywords.

Terms	Count	Terms	Count	Terms	Count	Terms	Count
global	1276	jobs	485	abilities	362	transformation	258
skills	1193	higher	482	years	351	retention	257
share	1155	skill	465	key	351	machine	255
education	1136	outlook	453	upskilling	337	service	252
learning	1063	reskilling	448	economic	327	hiring	246
surveyed	1009	growth	446	roles	323	comprehensive	239
organizations	959	effect	441	services	320	experience	229
talent	912	workers	436	world	319	student	225
job	903	digital	432	dei	318	potential	220
training	850	technology	430	teaching	311	inclusion	219
net	731	labour	408	average	306	source	217
business	620	students	399	environmental	302	ordered	214
data	611	social	393	managers	301	hours	213
companies	584	workforce	386	churn	299	consumers	212
impact	562	thinking	386	availability	299	online	210
industry	522	ai	376	new	291	displacer	207
improve	510	expected	374	economy	286	creative	207
future	502	management	371	institutions	281	components	206
development	487	report	362	percent	275	analytical	205
technologies	485	abilities	362	change	272	lifelong	199

is the result of the frequentist analysis of all the documents in the corpus. They are sorted according to the frequency of occurrence, which is indicated on the right.

This has been obtained by counting terms in the six documents, eliminating empty words according to the usual text analysis procedures (see Figure A2 in Appendix A for an illustrative word cloud).

Some relevant data can be highlighted. There is a greater frequency of terms that express actions that indicate the trend towards an education more focused on personal learning and the acquisition of skills (training, learning), as opposed to a more traditional teaching based on teaching and knowledge acquisition (teaching, knowledge). The dominant frequency of terms regards the acquisition of skills and abilities (skills, talent, reskilling), as opposed to the teaching of knowledge content.

The widespread presence of terms related to work and business (job, business, companies, labour, workforce, etc.) is not only a consequence of the fact that one of the five reports deals with the future of work, but also because they appear in the specific education reports. Technology appears as a relevant item, accompanied or not by terms also with an important presence related to the digital world and AI (net, technologies, digital, technology, online, etc.).

Very significant specific terms frequently appear, which can be interpreted directly with regard to the associated concepts. We highlight the following as representative of the topics on which the texts are focused, and which appear in descending order of frequency. All of them can be understood without further explanation: Environmental; Machine; Attitudes; Diversity; Thinking; Experience; Resilience; Ethics; Transformation; Inclusion; Empathy; IA; Creative; Lifelong; Quality; Accessibility.

We can highlight the following topics related to certain couples, listed in descending order by frequency. It is possible to find others at the time when it is necessary to study a specific topic. The following list only indicates some majority relationships and their possible interpretation.

• Education as a search for and development of talent: talent-availability; talent-development; talent-detection.
• Education based on the development of skills and abilities, which implies a paradigm shift towards practical approaches in teaching: skills-knowledge; skills-share; training-comprehensive; skills-engagement; share-reskilling; skills-training; skills-cognitive; skills-self
• Turn towards the knowledge-based economy and promoted through economic and social achievements: education-attainment; organizations-knowledge.
• Education and new technologies: Education-technologies; education-workforce; education-4.0.

Step 5. Resulting scenarios. Each method used in the previous steps produced a list of potential trends (some prioritised by numerical rates) that must be considered for future higher education strategy in Peru. Now is the time to consolidate these findings and present a comprehensive view of what the future may hold. Specifically, three scenarios were projected, outlining the possible evolution of higher education based on current trends.

• Adaptability and flexibility scenario: it is projected that, by 2030, 40% of programmes will use hybrid and flexible formats, allowing students to better manage their work and educational commitments.
• Integration of digital services scenario: by 2030, it is expected that the use of digital services and AI-based tools will increase by 50%, optimising the educational experience and improving administrative management.
• Ecosystem collaboration and networking scenario: a 30% increase in collaboration between educational institutions and actors in the productive sector is expected by 2030, promoting joint projects that enrich graduate education programmes.

Results of the trend analysis by thematic group:

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Adaptability: Adaptability is essential for educational institutions wishing to adapt to new student demands. By 2030, 40% of programmes are expected to adopt a hybrid format, demonstrating the growing need for personalisation and flexibility in educational approaches.

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Digital services: The adoption of digital services, including AI-based tools, is expected to grow by 50%, improving the efficiency and accessibility of educational programmes.

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People and Culture: This group focuses on student empowerment and active participation. Gen Z, a demographic seeking more dynamic and personalised learning experiences, showed a preference for digital content in 65% of cases.

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Ecosystems: Collaborative networks between educational institutions and the productive sector are essential for innovation. A 30% increase in inter-institutional agreements and joint projects is expected, which will enrich the PUCP’s educational offer.

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Collaboration: Cooperation between the different actors in the education ecosystem will be key to innovation. This collaboration includes not only academic institutions, but also companies in the productive sector that can support the development of skills relevant to the labour market.

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Work transformation: The work environment is being transformed by digitalisation and automation, which requires graduate education to provide advanced competencies in these areas. Students need to develop specific skills to navigate an increasingly automated labour market

4. Discussion

This study has identified a number of emerging trends that have the potential to significantly transform graduate education in the Peruvian context in general and at the Pontificia Universidad Católica del Perú (PUCP) in particular. These trends were identified through a mixed approach that combined generative AI tools, natural language processing tools and digital marketing resources, as well as our specific method based on semantic projections (Defly Compass). The aim of the analysis was to identify patterns of change and opportunities for innovation that would guide the educational offer towards a more flexible, personalised approach, oriented towards the demands of the current labour market [29,30,31].

Our multidimensional approach allowed us to accurately identify the six key trends that could significantly impact graduate education at PUCP and in Peru. These are the adaptability of learning models, the expansion of micro-credentials, the use of artificial intelligence to personalise learning, the demands of Generation Z, the transformation of work and the development of digital skills, and the disruption of the face-to-face–online dichotomy [36,37,38,39,40]. Each of these trends will then be developed and argued.

a. Adaptability of Learning Models and Expansion of Micro-credentials. The analysis showed a growing preference for adaptive and flexible learning models in response to students’ need to balance their studies with their personal and work commitments [41,42]. Between 2021 and 2023, according to data from Google Trends and Semrush, interest in flexible modalities will increase by 38% [43,44], while demand for micro-credentials will increase by 42%. This increase reflects a clear need for educational programmes that offer a more flexible structure, allowing students to acquire specific competencies quickly and efficiently [45].

Micro-credentials are presented as a strategic opportunity for PUCP, as they allow for constant updating of skills and better alignment with labour market demands [46]. Short programmes, targeted at specific competencies, are particularly attractive to professionals who want to improve their skills without committing to longer and more expensive programmes. This not only responds to global trends, but also positions PUCP as an institution ready to offer innovative educational solutions [36,39].

The increasing demand for micro-credentials represents a significant opportunity for PUCP to expand its academic offerings and adapt to global trends. Students increasingly value flexibility and the ability to acquire specific competencies in a short period of time, suggesting that the university should consider developing short, modular programmes that allow for rapid, internationally recognised certifications [30]. This strategy will not only facilitate the integration of students into the workforce, but will also allow the university to position itself as a leader in educational innovation [37,38,40].

This trend has been implemented through various recent initiatives that demonstrate its relevance for postgraduate higher education in Peru. One of these is the intermediate certification of the first year of a Master’s degree as a specialisation programme (Specialisation Diplomas at PUCP).

b. Generative Artificial Intelligence. The use of generative artificial intelligence (GAI) to personalise learning has been identified as a key factor in improving the educational experience at PUCP [29,31]. AI-based tools, such as personalised recommendation systems, can help identify individual student needs and adapt educational content to their rhythms and preferences [47]. This has the potential to optimise teaching, allowing resources to be used more efficiently and students to have a more effective and engaging learning experience [5].

The use of artificial intelligence not only facilitates the creation of personalised learning paths, but also enables the analysis of large amounts of educational data, which helps to predict behaviour and adjust pedagogical strategies in real time [37,38,40]. In this sense, ChatGPT and Voyant have been used to identify how AI can be implemented to improve the educational experience, demonstrating that advances in this area can have a direct positive impact on the teaching–learning process [48,49].

The personalisation of learning through AI allows for an approach that is more tailored to the individual needs of students, thereby improving the effectiveness of the educational process [30]. This student-centred approach is key to the transformation of higher education, and PUCP has the opportunity to lead this change in Peru [1]. The adoption of AI technologies will not only improve the quality of teaching, but will also allow the university to compete globally in terms of educational innovation [45].

c. Gen Z and New Learning Models. Generation Z, a cohort born in a digital context, presents new challenges and opportunities for higher education. Analysis shows that 65% of Gen Z students prefer more dynamic and interactive learning formats that involve digital tools and are customisable [50]. These students want more engaging, interactive and personalised educational experiences that meet their expectations for immediacy and access to real-time information [48,49]

This shift in student preferences means that PUCP must transform its teaching methods, integrate digital technologies more deeply and develop more flexible and dynamic learning environments [37,38,40]. Furthermore, Gen Z has a high affinity for social platforms and digital media, suggesting that institutions that do not adopt these formats risk losing their competitiveness in attracting new students [32,47].

Institutions must adapt to the expectations of Gen Z to attract and retain these students. This means offering greater integration of digital tools, such as interactive learning platforms, that are accessible and adaptable. PUCP has the opportunity to position itself as an innovative institution that meets the demands of modern students and attracts a new generation of students who value flexibility and digital access [48,49,50].

d. Work Transformation and Digital Skills. The transformation of work, driven by digitalisation and automation, is redefining the skills required for future professionals [51]. Advanced digital skills such as data analysis, programming and software development will be essential to adapt to an increasingly automated and data-driven work environment. Data from digital marketing analytics shows an increasing demand for these skills, highlighting the importance of PUCP integrating this knowledge into its graduate programmes [45].

PUCP should anticipate these changes in the labour market and offer programmes that incorporate advanced digital skills [51]. This will not only better prepare students for new work challenges, but also strengthen the university’s competitiveness by offering academic programmes that directly respond to the needs of the productive sector [52]. The integration of advanced digital skills will enable students to be more competitive in the global labour market.

e. Disrupting the Face-to-Face and Online Dichotomy. One of the most notable trends to emerge from the analysis was the growing disruption of the dichotomy between face-to-face and online teaching. The data showed that, between 2021 and 2023, there will be a 30% increase in interest in hybrid learning modalities. This approach allows for the best of both worlds: face-to-face interaction with flexible access to online resources. For PUCP, this trend represents an opportunity to consolidate its leadership in higher education by designing programmes that integrate digital and face-to-face components in a fluid manner [5].

Hybrid modalities not only meet students’ expectations, but also allow for greater inclusiveness, as students can access courses from anywhere and at any time. This flexible approach is particularly relevant in a context such as Peru, where inequalities in access to education remain a challenge.

This trend has been strongly adopted in postgraduate studies in Peru since the pandemic. There are several factors inherent in the country’s specific situation that favour its very rapid adoption. The first factor is the profile of the postgraduate student in Peru: almost all postgraduate students are part-time students, i.e., they work and study at the same time. The second factor is that Peru is a centralised country where postgraduate education is concentrated in the capital. As a result, the adoption of a hybrid modality in postgraduate education has been a very strong trend in the post-pandemic era.

The adoption of hybrid modalities allows PUCP to extend its reach by offering programmes that are accessible to a greater number of students without compromising the quality of educational interaction [5]. This flexibility is key to attracting new students who are looking for a balance between the convenience of digital access and the benefits of face-to-face interaction.

This study has provided a broad overview of the emerging trends that will affect postgraduate education at PUCP in the coming years. Micro-credentials, artificial intelligence, hybrid formats and personalisation of learning stand out as key areas that the university should focus on to adapt to the changing demands of students and the labour market [32,47,48,49].

By adapting its programmes to these trends, PUCP will not only strengthen its position as a leading institution in Peru, but will also be able to compete globally in terms of educational innovation [45,51].

5. Conclusions

The result of the analysis of the future of postgraduate education at the PUCP, which in principle can be extrapolated to the Peruvian university system, allows us to understand different aspects that will be crucial for the strategic design of universities in the coming years. The main methodological aspects of the study are presented below, followed by the direct conclusions on the variables that will influence higher education. The methodology used, included and related the following four main elements: semantic forecasting (semantic projections) based on academic databases such as Google Scholar, arXiv and Digital CSIC, which allowed an in-depth understanding of emerging trends based on the analysis of relevant scientific publications [43,44]; digital marketing analysis using tools such as Google Trends, Google Ads and Semrush to monitor the evolution of demand for specific educational trends in the digital environment [32,47]; classification of trends using the ASPECT model [28], which organises trends into six main thematic groups; and validation strategy documents prepared by Group 0 experts who assessed the relevance and stability over time of the trends identified.

With regard to higher education in Peru, the main conclusions are as follows. Adaptive and flexible educational models are on the rise, reflecting the need for students to balance their studies with personal and professional responsibilities. This scenario favours the growth of micro-credentials, which allow specific skills to be acquired in an agile and efficient way, in line with the demands of the labour market. For PUCP, the integration of these short and modular programmes represents a strategic opportunity to consolidate its leadership in educational innovation, improve the employability of students and attract more professionals.

Artificial intelligence (AI) also plays a key role in personalising learning. AI-based tools, such as recommendation systems and the analysis of educational data, make it easier to tailor content to students’ individual needs, optimise resources and improve the educational experience. For their part, Generation Z, digital natives, are demanding more dynamic, interactive and personalised learning formats that take advantage of digital technologies and meet their expectations for immediacy. This provides an opportunity to innovate by integrating interactive digital platforms and flexible methodologies to attract and retain young people.

In terms of the labour market, digitalisation and automation have changed the skills required. The demand for skills such as programming, data analysis and software development underlines the importance for universities to include these areas in their graduate programmes. In this way, students will be better prepared for the challenges of the labour market and universities will gain global competitiveness.

Finally, hybrid modalities that combine face-to-face and online teaching have gained popularity, especially in the post-pandemic context. This approach offers flexibility and accessibility and corresponds to the profile of Peruvian graduate students, who tend to work and study at the same time. The adoption of hybrid programmes makes it possible to broaden the scope of university teaching, promote inclusiveness and maintain high standards of educational interaction.

In conclusion, the adoption of a working methodology based on micro-credentials, personalisation through AI, dynamic formats for Generation Z, advanced digital skills and hybrid modalities will allow Peruvian universities to adapt to the changing demands of the market and the expectations of new students.