Career and technical education play a significant role in reducing high school and college dropouts as well providing necessary skills and opportunities to make suitable career decisions. The recent technological advances have benefited the education sector tremendously with the introduction of exciting innovations including virtual and augmented reality. The benefits of NL and game-based learning are well-established in the literature. However, their implementation has been limited to the education sector. In this research, the design and implementation of a Narrative Integrated Career Exploration (NICE) platform is discussed. The platform contains four playable tracks allowing students to explore careers in artificial intelligence, cybersecurity, internet of things, and electronics. The tracks are carefully designed with narrative problem-solving reflecting contemporary real-world challenges. To evaluate the perceived usefulness of the platform, a case study involving university students was performed. The results clearly reflect students’ interest in narrative and game-based career exploration approaches.

Career and Technical Education Market Outlook

According to our latest research, the global Career and Technical Education (CTE) market size reached USD 63.2 billion in 2024, with a robust year-on-year growth driven by increasing demand for skilled labor and workforce readiness. The market is forecasted to expand at a CAGR of 8.7% from 2025 to 2033, resulting in a projected market value of USD 133.7 billion by 2033. This growth trajectory is propelled by rapid technological advancements, the integration of digital learning solutions, and a global emphasis on employability and industry-aligned education.

One of the primary growth factors for the Career and Technical Education market is the escalating need for workforce development in response to shifting industry requirements. As automation and digital transformation continue to disrupt traditional job roles, there is an increasing emphasis on upskilling and reskilling workers to align with new industry standards. Governments and private enterprises worldwide are investing significantly in CTE programs to bridge the skills gap, particularly in sectors such as healthcare, IT, manufacturing, and finance. The integration of emerging technologies like artificial intelligence, robotics, and cloud computing into curricula is further enhancing the relevance and appeal of CTE, making it a pivotal component in national education and economic strategies.

Another critical driver of market expansion is the proliferation of online and hybrid learning modes, which have revolutionized access to Career and Technical Education. The COVID-19 pandemic catalyzed the adoption of digital platforms, enabling institutions to reach a broader audience, including working professionals and underserved communities. This shift has led to the development of flexible, modular, and competency-based learning pathways tailored to diverse learner needs. Edtech companies and traditional educational institutions are collaborating to deliver interactive, industry-aligned content through virtual labs, simulations, and real-time assessments. As a result, the CTE market is experiencing increased enrollment and higher completion rates, reinforcing its role in lifelong learning and career mobility.

The role of EdTech in Career and Technical Education cannot be overstated. As digital platforms and tools become more sophisticated, they are transforming the way CTE programs are delivered and consumed. EdTech solutions are enabling personalized learning experiences, where students can progress at their own pace and access resources tailored to their individual learning styles. Through the use of virtual labs, simulations, and interactive content, EdTech is making education more engaging and effective, particularly for technical and vocational subjects that benefit from hands-on practice. This technological integration is crucial for preparing students for the digital economy, where adaptability and tech-savviness are key competencies.

Furthermore, strong collaboration between industry stakeholders, educational institutions, and government bodies is driving innovation and standardization within the Career and Technical Education market. Public-private partnerships are fostering the creation of specialized curricula, apprenticeships, and certification programs that align with current and future labor market demands. These initiatives are not only enhancing the employability of graduates but also ensuring that CTE programs remain agile and responsive to technological and economic shifts. Additionally, policy reforms and increased funding for vocational and technical education are accelerating the adoption of CTE across both developed and emerging economies.

Regionally, North America continues to dominate the global CTE market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, benefits from robust government support, advanced infrastructure, and a strong culture of industry-education collaboration. Meanwhile, Asia Pacific is witnessing the fastest growth, fueled by large student populations, rapid industrialization, and increasing investments in education technology. Europe maintains a significant presence due to its well-established vocational training systems and strategic focus on workforce modernization. Other regions, su

The Continuing Education Market was valued at USD 42.3 Billion in 2025 and is projected to grow to USD 70 Billion by 2035, at a CAGR of 5.2%. Continuing Education Market Overview: The Continuing Education Market Size was valued at 40.2 USD Billion in 2024. The Continuing Education Market is expected to grow from 42.3 USD Billion in 2025 to 70 USD Billion by 2035. The Continuing Education Market CAGR (growth rate) is expected to be around 5.2% during the forecast period (2025 - 2035). Key Continuing Education Market Trends Highlighted The Global Continuing Education Market is experiencing significant growth driven by various key market drivers. One of the main drivers is the increasing demand for lifelong learning due to rapid changes in technology and workforce requirements. Organizations are prioritizing upskilling and reskilling their employees to remain competitive in an evolving job market, leading to a higher enrollment in continuing education programs. As professionals seek to enhance their skills, institutions have expanded online offerings, which has been essential in increasing accessibility to education globally. Emerging opportunities are being explored within the market, particularly in the integration of advanced technologies such as artificial intelligence and virtual reality in educational programs.These technologies not only enrich the learning experience but also attract a younger demographic that values innovative and flexible learning options. Moreover, global partnerships between educational institutions and industries are on the rise, promoting tailored programs that meet the specific needs of employers. This collaboration is essential for bridging the skills gap observed in many sectors worldwide. In recent times, a notable trend has been the surge in demand for micro-credentials and short courses that address specific skill gaps rather than traditional degrees. This shift reflects a broader recognition of the importance of practical skills in the job market.The ongoing impact of the COVID-19 pandemic has also accelerated the adoption of digital learning platforms, which allow individuals to continue their education despite geographical challenges or time constraints. Collectively, these trends indicate a robust and evolving landscape for the Global Continuing Education Market, with a growing focus on flexible and technology-driven learning solutions. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Continuing Education Market Segment Insights: Continuing Education Market Regional Insights The Global Continuing Education Market is diversely segmented regionally, reflecting varying demands across distinct geographies. North America leads with a prominent valuation, showcasing a stronghold in the market. The region demonstrates a robust demand for continuing education driven by the increasing necessity for workforce upskilling and professional development, which underscores its significance as a market leader. Europe follows closely, where steady expansion is noted as educational institutions increasingly adapt to flexible learning methods to cater to adult learners.The Asia-Pacific (APAC) region is experiencing strong growth, driven by increasing government initiatives emphasizing education as a cornerstone for economic improvement and workforce competitiveness. South America shows moderate increase as educational institutions and corporate training programs begin to evolve, focusing on lifelong learning opportunities. Meanwhile, the Middle East and Africa (MEA) face gradual decline, influenced by infrastructural challenges and varying educational policies, which impact the growth trajectory. Overall, the demand for continuing education is reshaping the learning landscape across these regions, highlighting the importance of adapting to market trends and regional needs. Source: Primary Research, Secondary Research, WGR Database and Analyst Review North America : The Continuing Education Market in North America is driven by the increasing integration of AIoT technologies in sectors such as healthcare and smart manufact

Dataset Description: This dataset contains information about students enrolled in an academy, including their personal details, academic scores, extracurricular activities, and career aspirations. Features Information: id: Unique identifier for each student. first_name: First name of the student. last_name: Last name of the student. email: Email address of the student. gender: Gender of the student (male/female). part_time_job: Indicates whether the student has a part-time job (True/False). absence_days: Number of days the student has been absent. extracurricular_activities: Indicates whether the student participates in extracurricular activities (True/False). weekly_self_study_hours: Number of hours the student spends on self-study per week. career_aspiration: Aspirational career path of the student. math_score: Score achieved by the student in mathematics. history_score: Score achieved by the student in history. physics_score: Score achieved by the student in physics. chemistry_score: Score achieved by the student in chemistry. biology_score: Score achieved by the student in biology. english_score: Score achieved by the student in English. geography_score: Score achieved by the student in geography. Usage in ML Projects: This dataset can be used for various machine learning projects, including but not limited to: Predicting students' career aspirations based on their academic performance and extracurricular activities. Predicting students' absenteeism based on their personal and academic characteristics. Exploring the relationship between students' academic scores and their participation in extracurricular activities or part-time jobs. *Note: * Before using this dataset for any analysis or machine learning projects, it's essential to preprocess the data, handle missing values, encode categorical variables, and split the data into training and testing sets appropriately. Additionally, ensure compliance with any privacy or ethical considerations when working with personal data such as email addresses.

We describe alignment between high school career and technical education (CTE) and local labor markets across five states—Massachusetts, Michigan, Montana, Tennessee, and Washington. We find that CTE is partially aligned with local labor markets. A 10-percentage-point higher share of local jobs related to a CTE career cluster is associated with a 3-point higher rate of CTE concentration in that cluster. Women and students from racial or ethnic minority groups are better aligned with local employment than men, in part due to their selection of CTE fields like Education & Training, Health Science, and Hospitality & Tourism, which correspond with a large portion of the workforce in almost every metro area. We find more limited evidence of dynamic, short-term adjustments in CTE after changes in local labor markets. A small degree of realignment lags the labor market by two-to-three years and is only observed following changes in college-level employment.

This dataset contains 15,000 student records and 45 columns designed to support research on entrepreneurship education, student competencies, career development, personalized educational resources, employability, and career outcomes in higher education.

The dataset integrates student demographic information, academic performance, entrepreneurship education, business knowledge, financial literacy, innovation skills, creativity, leadership, communication, teamwork, digital skills, learning behavior, career interests, personality traits, extracurricular participation, innovation projects, hackathon participation, internship experience, industry mentor interactions, employability skills, interview readiness, and career resource information.

It provides a comprehensive representation of factors associated with entrepreneurial potential and career development among college students. The dataset can support studies focused on entrepreneurship education quality, student skill development, personalized career resources, employability assessment, university–industry collaboration, and data-driven decision-making in higher education.

Number of Records: 15,000

Number of Columns: 45

Columns Description:

student_id: Unique identification code assigned to each student record.

age: Age of the student in years.

gender: Gender category of the student.

academic_year: Current academic year of the student.

degree_program: Degree program in which the student is enrolled.

department: Academic department associated with the student.

cgpa: Cumulative academic performance of the student.

attendance_percentage: Percentage of academic sessions attended by the student.

academic_performance_score: Overall score representing the student's academic performance.

entrepreneurship_course_count: Number of entrepreneurship-related courses completed by the student.

entrepreneurship_course_score: Performance score obtained in entrepreneurship education courses.

business_knowledge_score: Score representing the student's understanding of business concepts and practices.

financial_literacy_score: Score indicating the student's knowledge of financial concepts and financial management.

innovation_skill_score: Score representing the student's ability to develop innovative ideas and solutions.

creativity_score: Score indicating the student's creative thinking ability.

risk_taking_score: Score representing the student's willingness to take calculated risks.

leadership_score: Score indicating the student's leadership and decision-making abilities.

problem_solving_score: Score representing the student's ability to analyze and solve problems.

communication_skill_score: Score indicating the student's verbal and interpersonal communication abilities.

teamwork_score: Score representing the student's ability to collaborate effectively with others.

digital_skill_score: Score indicating the student's competency in using digital technologies and tools.

learning_hours_per_week: Number of hours spent by the student on learning activities each week.

online_learning_activity: Score representing the student's participation in online learning activities.

assignment_completion_rate: Percentage of assigned academic work completed by the student.

learning_engagement_score: Overall score representing the student's involvement and engagement in learning activities.

career_interest_domain: Primary career field or professional domain preferred by the student.

entrepreneurship_interest_score: Score indicating the student's level of interest in entrepreneurship.

career_clarity_score: Score representing the student's understanding and clarity of career goals.

personality_openness_score: Score indicating openness to new experiences, ideas, and opportunities.

personality_conscientiousness_score: Score representing responsibility, organization, and goal-oriented behavior.

personality_extraversion_score: Score indicating the student's level of social interaction and outgoing behavior.

extracurricular_activity_count: Number of extracurricular activities participated in by the student.

innovation_project_count: Number of innovation-oriented projects completed by the student.

hackathon_participation_count: Number of hackathons in which the student has participated.

internship_experience_months: Total duration of the student's internship experience measured in months.

industry_mentor_interaction_count: Number of interactions the student has had with industry mentors.

employability_skill_score: Overall score representing skills associated with employment readiness.

interview_readiness_score: Score indicating the student's preparedness for employment interviews.

resource_demand_score: Score representing the level of educational and career resources required by the student.

resource_availability_score: Score indicating the availability of relevan...

The Job Hunting Education Service Platform Market was valued at USD 7.73 Billion in 2025 and is projected to grow to USD 12.4 Billion by 2035, at a CAGR of 4.9%. Job Hunting Education Service Platform Market Overview: The Job Hunting Education Service Platform Market Size was valued at 7.37 USD Billion in 2024. The Job Hunting Education Service Platform Market is expected to grow from 7.73 USD Billion in 2025 to 12.4 USD Billion by 2035. The Job Hunting Education Service Platform Market CAGR (growth rate) is expected to be around 4.9% during the forecast period (2025 - 2035). Key Job Hunting Education Service Platform Market Trends Highlighted The Global Job Hunting Education Service Platform Market is experiencing significant transformations driven by various factors. One of the key market drivers is the increasing demand for skill enhancement and personalized learning experiences as individuals seek to stand out in a competitive job market. With the rise of remote work and digital job applications, there is an enthusiastic adoption of online platforms that facilitate job searching and skill development. Furthermore, the integration of technology, such as artificial intelligence and machine learning, enhances the efficiency of these platforms, enabling users to access tailored job recommendations and training programs.Recent trends indicate that job seekers are increasingly gravitating towards platforms that not only offer job listings but also provide comprehensive educational resources. This shift reflects a growing recognition of the need for continuous professional development in the context of rapidly evolving job requirements. The demand for blended learning experiences, characterized by a combination of online tutorials, webinars, and mentorship, is shaping the offerings of these platforms and creating opportunities for new entrants in the market. Opportunities in the Global Job Hunting Education Service Platform Market are ripe for exploration. The continuous evolution in workforce dynamics, such as the gig economy and increasing use of freelance work, presents possibilities for platforms to cater to diverse career paths.By addressing the specific needs of various demographic groups, including recent graduates and mid-career professionals, companies can create tailored offerings that resonate well with a broad audience. As educational institutions and enterprise organizations increasingly collaborate with these platforms, it offers a chance to bridge the gap between education and employment, ultimately shaping a more equipped workforce for the future. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Job Hunting Education Service Platform Market Segment Insights: Job Hunting Education Service Platform Market Regional Insights The Global Job Hunting Education Service Platform Market exhibits varied regional dynamics, with North America dominating the landscape significantly. In 2024, North America was valued at 3 USD Billion and is projected to reach 5 USD Billion by 2035, showcasing consistent growth and a robust demand for job hunting services in this region. Europe is experiencing steady expansion in its market, driven by an increasing number of online platforms catering to job seekers' needs and career development. Meanwhile, the Asia-Pacific (APAC) region is demonstrating a moderate increase, fueled by rapid urbanization and a growing emphasis on skill development among the workforce.South America shows signs of gradual growth as job hunters seek innovative platforms in response to economic shifts. The Middle East and Africa (MEA) present a diverse landscape of opportunities, with a focus on enhancing access to education and employment resources. Given the rise in unemployment rates globally, the urgent need for effective job hunting education services remains paramount, creating a fertile ground for business growth across these regions and highlighting the importance of understanding regional market variations within the Global Job Hunting Education Service Platform Market. Source: Primary Research, Secondary Research, WGR Database and Analyst

The Survey on Humanities Graduate Education and Alternative Academic Careers, 2012 investigates perceptions about career preparation provided by humanities graduate programs. The study was carried out by the Scholarly Communication Institute. The SCI administered a broad survey of humanities-trained respondents who self-identify as working in alternative academic careers (non-academic, non-tenured or tenured track), as well as their employers. Part One (Main Survey (Public-Use)) of the collection contains data of humanities-trained respondents working in alternative academic careers. Respondents were asked about their degree(s), career, methods course, value of degree(s), preparedness for position, training at work, job resources, and job skills. Part Two (Employer Survey (Public-Use)) contains data from a survey of the respondents' employers. Employers were asked about their humanities employees' performance, competencies, education, preparedness, and any training the employees needed. Part Three (Main Survey (Restricted-Use)) is a restricted data file that contains an additional 58 variables not included in Part One. Variables in Part Three include variables such as race, gender, geography, birthday, marital status, and number of dependents.

The English E-Learning market shows robust expansion, driven by demand for skill development. Analyze 12.68% CAGR and key segment trends impacting global education through 2033.

This dataset captures detailed Multiple Intelligence (MI) scores across key domains—Linguistic, Logical-Mathematical, Spatial, Interpersonal, Intrapersonal, Musical, Bodily-Kinesthetic, and Naturalist—for a diverse group of students. Each student record is also associated with a corresponding career or job profession, enabling researchers and machine learning practitioners to analyze relationships between intelligence patterns and career pathways. The dataset is designed to support academic research, predictive modeling, clustering, student profiling, and career recommendation systems. It can serve as a foundational asset in building AI-powered career guidance platforms, educational analytics dashboards, psychometric evaluation engines, and decision-support tools.

Key Features:- ✔️ Multiple Intelligence scores (8 dimensions per student) ✔️ Mapped job/career roles for each student ✔️ Performance indicators (P1–P8) ✔️ Ideal for supervised and unsupervised learning ✔️ Supports classification, clustering, recommendation systems, and dashboarding

Potential Use Cases:- Career prediction models using MI scores Student clustering based on intelligence patterns Psychometric analysis for educational research Dashboard development for counselors or institutions ML explainability studies linking traits to job roles Benchmark dataset for academic assignments or hackathons

Discover the booming one-to-one course counseling market! Learn about its $15 billion (2025) size, 12% CAGR, key players like Chegg and Kaplan, and regional growth trends in this in-depth market analysis. Explore online vs. offline tutoring, skill courses, and more.

snapshot-tab-pane Higher Education Market Size 2026-2030The higher education market size is valued to increase by USD 140.65 billion, at a CAGR of 19.4% from 2025 to 2030. Intensifying global demand for skilled workforce will drive the higher education market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 41.9% growth during the forecast period.By Learning Method - Online segment was valued at USD 56.71 billion in 2024By End-user - Private colleges segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 188.58 billionMarket Future Opportunities: USD 140.65 billionCAGR from 2025 to 2030 : 19.4%Market SummaryThe higher education market is undergoing a profound transformation, moving beyond traditional enrollment expansion to address the imperatives of a digital-first economy. This evolution is driven by the persistent global demand for advanced skills, compelling institutions to innovate.A pivotal trend is the unbundling of monolithic degrees in favor of more flexible, career-aligned educational pathways, including micro-credentials and stackable credentials, which cater to the need for lifelong learning.In one business scenario, an institution leverages predictive analytics models for its strategic enrollment management to identify at-risk students, enabling proactive interventions that improve student success and retention rates by double digits. However, the sector grapples with a significant skills gap between academic curricula and employer needs, alongside a student affordability crisis.Success is now increasingly defined by an institution's ability to forge strong industry connections, embrace technological innovation like personalized learning paths through learning management systems, and provide learners with tangible returns on their educational investment through improved post-graduation outcomes and socioeconomic mobility.What will be the Size of the Higher Education Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Higher Education Market Segmented?The higher education industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.Learning methodOnlineOfflineHybridEnd-userPrivate collegesState universitiesCommunity collegesCoursesUndergraduateMastersPhDGeographyNorth AmericaUSCanadaMexicoEuropeUKGermanyFranceAPACChinaIndiaJapanSouth AmericaBrazilArgentinaMiddle East and AfricaSaudi ArabiaUAESouth AfricaRest of World (ROW)By Learning Method InsightsThe online segment is estimated to witness significant growth during the forecast period.The online segment of the higher education market has become a significant and expanding modality, propelled by learner demand for flexible and accessible learning.Characterized by continuous innovation in pedagogical approaches, this segment serves non-traditional students, including working professionals, who require educational models compatible with their schedules.Institutions are enhancing the quality of this experience by leveraging student engagement analytics and improving their academic program portfolio.Advanced online program management is becoming standard, with a notable 75% of students now preferring degree programs that grant credit for micro-credentials.This reflects a major shift where modular, career-focused online learning, supported by robust student support services and effective learning outcome assessment, is an integral component of formal education and lifelong learning. Get Free SampleThe Online segment was valued at USD 56.71 billion in 2024 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisNorth America is estimated to contribute 41.9% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Higher Education Market Demand is Rising in North America Get Free SampleThe global higher education market exhibits significant regional divergence, shaping its overall competitive landscape. North America remains the most mature region, accounting for over 41% of the market's incremental growth, driven by its prestigious institutions and strong research infrastructure.In contrast, the APAC region is the most dynamic, with growth rates exceeding 20% annually, fueled by a rising middle class and government investment in creating global education hubs.This has intensified global student recruitment efforts and the need for strategic enrollment management. Institu

This dataset contains anonymized survey responses from 140 early-career professionals examining preferences for game-based and AI-supported learning formats in workplace training contexts. Participants rated the importance of professional competencies, instructional formats, motivational incentives, and game mechanics, and indicated preferences for session duration, frequency, and device access. Multi-select responses are encoded as binary indicator columns (0/1); Likert items are recorded on a 1–5 scale. The data support descriptive analysis and cross-tabulation by gender, organizational role, and career stage, and are suitable for secondary use in research on adult learning, instructional design, and digital professional development.

The increasing complexity of career decision-making, shaped by rapid technological advancements and evolving job markets, highlights the need for more responsive and data-informed post-diploma guidance. Machine learning (ML), a core component of artificial intelligence, is gaining attention for its potential to support personalized educational and career decisions by analyzing academic records, individual preferences, and labor market data. Despite growing interest, research in this field remains fragmented and methodologically diverse. This scoping review maps the application of ML in post-diploma guidance by examining the types of models used, data sources, reported outcomes, and ethical considerations related to fairness, privacy, and transparency. A systematic search of Scopus and Web of Science was conducted, with the final search completed on December 31, 2023. Twenty-one studies met the inclusion criteria, primarily employing supervised or mixed-method ML techniques to develop recommendation systems or predictive models. While several contributions report positive technical performance, evidence on educational effectiveness and user impact is limited. Ethical concerns such as bias, opacity, and limited explainability are acknowledged but not consistently addressed. The findings point to the need for more rigorous empirical research, greater methodological transparency, and the integration of educational perspectives to ensure that ML-based systems for career guidance are used responsibly and with clear added value.

This dataset contains the original survey data for a study examining the effects of career outcome expectations on learning attitudes and career switching intentions among vocational college students in construction-related majors in Central China. A total of 679 valid responses were collected from seven vocational colleges. The dataset includes demographic variables (gender, major, grade, hometown type), response time, and item-level responses for four scales: Career Outcome Expectations (5 items), Learning Attitudes (4 items), Career Switching Intentions (3 items), and Professional Identity (4 items), plus one attention check item. All items were rated on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). Detailed variable labels and coding are provided in the "Variable_Labels" sheet. Data cleaning procedures are described in the "Data_Cleaning" sheet. The dataset is shared to support replication and transparency in vocational education research.

The recent movement underscoring the importance of career taxonomies has helped usher in a new era of transparency in PhD career outcomes. The convergence of discipline-specific organizational movements, interdisciplinary collaborations, and federal initiatives has helped to increase PhD career outcomes tracking and reporting. Transparent and publicly available PhD career outcomes are being used by institutions to attract top applicants, as prospective graduate students are factoring in these outcomes when deciding on the program and institution in which to enroll for their PhD studies. Given the increasing trend to track PhD career outcomes, the number of institutional efforts and supporting offices for these studies have increased, as has the variety of methods being used to classify and report/visualize outcomes. This report comprehensively synthesizes existing PhD career taxonomy tools, resources, and visualization options to help catalyze and empower institutions to develop and publish their own PhD career outcomes. Similar fields between taxonomies were mapped to create a new crosswalk tool, thereby serving as an empirical review of the career outcome tracking systems available. Moreover, this work spotlights organizations, consortia, and funding agencies that are steering policy changes toward greater transparency in PhD career outcomes reporting. Such transparency not only attracts top talent to universities, but also propels research progress and technological innovation forward. Therefore, university administrators must be well-versed in government policies that may impact their PhD students. Engaging with government relations offices and establishing dialogues with policymakers are crucial steps toward staying informed about relevant legislation and advocating for more resources. For instance, much of the recent science legislation in the U.S. Congress, including the Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act, significantly impacts federal agency programs influencing universities. To ensure sustained development, it is imperative to support initiatives that enhance transparency, both in terms of legislation and resources. Increased funding for programs supporting transparency will aid legislatures and institutions in staying informed and responsive. Many efforts presented in this publication have received support from federal and state governments or philantrophic sources, underscoring the need for multifaceted support to initiate and perpetuate this level of systemic change.

Higher education institutions have invested in career counselling services to facilitate students’ transition from university to the workforce by developing employability skills. Career services increasingly offer technology-driven services that support specific competencies but face challenges in helping students reflect on their goals and create comprehensive plans that foster overall employability. This design-based mixed-method study focused on designing, implementing, and evaluating features of an online career services platform that supports stages of reflective practice for competence development: awareness, analysis, goal setting, action, and reflection. Participants in the design phase were 7 students and 4 career counsellors, whereas 9 students participated in a formative evaluation. This paper presents the results of the first cycle of this design-based research. This study supports the pedagogical value of organising online career services platforms as reflective practices that foster competence development. The iterative design-based research process enabled a structured integration of pedagogical principles with educational technology. The findings show that students perceive the online platform as effective and usable. In addition, the study also underscores the importance of usability, institutional integration, and ongoing guidance, such as prompts, feedback mechanisms, and collaborative features, to further enhance the platform’s potential in fostering employability competences in higher education.

Abstract

Opportunity-focused, high-growth entrepreneurship and science-led innovation are crucial for continued economic growth and productivity. Working in these fields offers the opportunity for rewarding and high-paying careers. However, the majority of youth in developing countries do not consider either as job options, affecting their choices of what to study. Youth may not select these educational and career paths due to lack of knowledge, lack of appropriate skills, and lack of role models. We provide a scalable approach to overcoming these constraints through an online education course for secondary school students that covers entrepreneurial soft skills, scientific methods, and interviews with role models.

The study comprises three experimental trials provided Before and during COVID-19 pandemic in different regions of Ecuador. This catalog entry includes data from Experiment 3: Coastal Educational Regime (Régimen Costa) 2020/2021. The data from the other two experiments are also available in the catalog.

Experiment 3: Coastal Educational Regime (Régimen Costa) 2020/2021

A randomized experiment conducted in high schools in Ecuador as rapid fire response to the hurdles of COVID-19 for the Coastal Educational regimes schools (Régimen Costa); Students finish the program in December 2020). The intervention is an online education course that covers entrepreneurial soft skills, scientific methods, and interviews with role models. This course is taken by students at home during the COVID-19 pandemic under teachers’ supervision. We work mostly with 14-22-year-old students (16,441 students) in 598 schools assigned to the program. We randomly assign schools either to treatment (and receiving the entrepreneurship courses online), or placebo-control (receiving a placebo treatment of online courses from standard curricula) groups. We also cross-randomize the role models and evaluate set of nimble interventions to increase take-up. The details of intervention can be found in AEA registry: Asanov, Igor and David McKenzie. 2021. Scaling up virtual learning of online learning in high schools. AEA RCT Registry. March 23 Merged datasets from the baseline, midline, endline survey for each experiment administrated through online learning platform in school during normal educational hours before COVID-19 pandemic or at student’s home during COVID-19 pandemic are documented here. The detailed information about the questioner and each item can be found in the codebooks (Baseline 1, Baseline 2, Midline, Endline 1, Endline 2) for corresponding experiments.

Geographic coverage

Experiment 3: Coastal Educational Regime (Régimen Costa) 2020/2021 We cover students of last year of education in School K12 of technical specialization (Bachillerato técnico) that study in Coastal Educational Regime (Régimen Costa) 2020/2021, suppose to finish their education in school in March 2021 and we capable to register on the online platform. The schools in highlands educational regime covered in this experiment scatter over the next educational zones 1, 2, 3, 4, 5, 6, 7, 8, 9.
Taken together in the experiment 2,3 we offered the program across all Ecuador to schools that have technical specialization track.

Analysis unit

Student

Kind of data

Sample survey data [ssd]

Sampling procedure

All students in selected schools who were present in classes filled out the baseline questionnaire

Mode of data collection

Internet [int]

Research instrument

Questionnaires We execute three main sets of questioners. A. Internet (Online Based survey)

The survey consists of a multi-topic questionnaire administered to the students through online learning platform in school during normal educational hours before COVID-19 pandemic or at home during the COVID-19 pandemic. We collect next information: 1. Subject specific knowledge tests. Spanish, English, Statistics, Personal Initiative (only endline), Negotiations (only endline). 2. Career intentions, preferences, beliefs, expectations, and attitudes. STEM and entrepreneurial intentions, preferences, beliefs, expectations, and attitudes. 3. Psychological characteristics. Personal Initiative, Negotiations, General Cognitions (General Self-Efficacy, Youth Self-Efficacy, Perceived Subsidiary Self-Efficacy Scale, Self-Regulatory Focus, Short Grit Scale), Entrepreneurial Cognitions (Business Self-Efficacy, Identifying Opportunities, Business Attitudes, Social Entrepreneurship Standards). 4. Behavior in (incentivized) games: Other-regarding preferences (dictator game), tendency to cooperate (Prisoners Dilemma), Perseverance (triangle game), preference for honesty, creativity (unscramble game). 5. Other background information. Socioeconomic level, language spoken, risk and time preferences, trust level, parents background, big-five personality traits of student, cognitive abilities. Background information (5) collected only at the baseline. B. First follow-up Phone-based Survey Zone 2, Summer (Phone Based). The survey replicates by phone shorter version of the internet-based survey above. We collect next information: 1. Subject specific knowledge tests.
2. Career intentions, preferences, beliefs, expectations, and attitudes. 3. Psychological characteristics

C. (Second) Follow-up Phone-Based Survey, Winter, Zone 2, Highlands Educational Regime.

We execute multi-topic questionnaire by phone to capture the first life-outcomes of students who finished the school. We collect next information:

1. Life Outcome 1- Education. The set of questions that aims to measure the learning success, career/study intentions, propensity to plan and approach others with studying tasks, entrepreneurial intentions.
2. Life Outcome 2- Labor. The set of questions that aims to measure employment status and income, job searching behavior, time devoted for working/business, salary expectations and knowledge about the careers, self-initiated contribution to the family.
3. Personal Initiative/Negotiations related and other measures. The set of questions that aim to measure level of personal initiative, negotiation strategies, pregnancy rate, gender stereotypes, math/STEM self-efficacy, gender attitudes, parent-student communication effects.

Cleaning operations

Data Editing A. Internet, Online-based surveys. We extracted the raw data generated on online platform from each experiment and prepared it for research purposes. We made several pre-processing steps of data: 1. We transform the raw data generated on platform in standard statistical software (R/STATA) readable format. 2. We extracted the answer for each item for each student for each survey (Baseline, Midline, Endline). 3. We cleaned duplicated students and duplicated answers for each item in each survey based on administrative data, performance and information given by students on platform. 4. In case of baseline survey, we standardized items/scales but also kept the raw items.

B. Phone-based surveys. The phone-based surveys are collected with help of advanced CATI kit. It contains all cases (attempts to call) and indication if the survey was effective. The data is cleaned to be ready for analysis. The data is anonymized but contains unique anonymous student id for merging across datasets.

AI Career Coaches Market Outlook

According to our latest research, the Global AI Career Coaches market size was valued at $1.2 billion in 2024 and is projected to reach $9.7 billion by 2033, expanding at a CAGR of 26.8% during the forecast period of 2025–2033. This remarkable growth trajectory is driven primarily by the increasing digitalization of career development services and the rising demand for personalized, data-driven career guidance among students, professionals, and enterprises globally. The proliferation of artificial intelligence technologies across human resource management and educational platforms has catalyzed the adoption of AI career coaches, offering tailored solutions that address skill gaps, streamline job matching, and enhance employability in a rapidly evolving labor market.

Regional Outlook

North America currently holds the largest share of the AI career coaches market, accounting for nearly 38% of global revenues in 2024. This dominance is underpinned by the region’s mature technology infrastructure, high penetration of AI-driven solutions, and a robust ecosystem of startups and established HR technology providers. The United States, in particular, has witnessed significant adoption among enterprises and educational institutions seeking to optimize talent acquisition and development processes. Furthermore, proactive government policies supporting digital transformation in education and workforce development, coupled with a culture that values lifelong learning, have solidified North America’s leadership in the global AI career coaches market.

Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 31.2% between 2025 and 2033. This exceptional growth is fueled by massive investments in EdTech, increasing internet penetration, and a burgeoning middle class eager for career advancement opportunities. Countries such as China, India, and Singapore are at the forefront, leveraging AI career coaches to address the skill mismatch between graduates and industry requirements. The region’s dynamic labor market, coupled with government-led initiatives to integrate AI into education and workforce training programs, is accelerating the adoption of AI-based career guidance tools, particularly among students and young professionals.

In contrast, emerging economies across Latin America, the Middle East, and Africa are experiencing a more gradual uptake of AI career coaches, primarily due to infrastructural challenges, limited digital literacy, and varying degrees of policy support. However, localized demand is rising as governments and private sector players recognize the potential of AI to democratize access to career guidance and bridge employment gaps. Pilot programs and partnerships with international EdTech firms are helping to overcome adoption barriers, but issues such as data privacy, language localization, and affordability remain critical challenges that need to be addressed to unlock the full potential of AI career coaching in these regions.

Report Scope

Discover the booming global vocational skills training market, projected to reach $20.11 billion by 2033 with a CAGR of 7-10%. This in-depth analysis explores market size, key drivers, trends, restraints, and regional breakdowns, highlighting leading companies and emerging opportunities in online and offline training. Learn about upskilling, reskilling, and the future of vocational education.