This statistic shows survey respondents answer to a question about how they preferred to learn about nonprofit organizations in the United States. This question was specifically asked to people from generation Y aged 20 to 35 years (also referred to as Millennials). 65 percent of respondents said they preferred to learn about nonprofit organizations through their websites.

Abstract This study aims to identify the predominant career profiles among Generation Y in Brazil and investigate the influence of demographic and professional characteristics in these profiles. Career profiles are defined as combinations of the presence of Boundaryless and Protean career attitudes. This study is descriptive, the sample is non-probabilistic and intentional and consists of 2,376 people. Cluster Analysis and Chi-square tests were used. The results show that people from Generation Y have a high presence of attitudes related to new careers and that these new careers are associated with high education and high income, mainly the Boundaryless career. However, there is a small portion of the sample that doesn’t present new career attitudes, indicating that this is just a movement and cannot be considered a reality for all studied subjects.

In 2020, around 75.5 percent of generation Z internet users in Thailand participated in e-learning. This was followed by generation Y at 50.4 percent. The internet has become an essential part of Thai people's lifestyle of all ages. The number of internet users in Thailand was forecasted to reach almost 62 million by 2025.

Abstract copyright UK Data Service and data collection copyright owner.

Researchers of Tomorrow (RoT) was a three-year study, sponsored by the British Library (BL) and the Joint Information Systems Committee (JISC). The study was carried out by Education for Change, with The Research Partnership. The study tracked a cohort of 70 young 'Generation Y' doctoral students (the children of the 'Baby Boomers'), defined in this study as those born between 1982 and 1994. The students were based at UK colleges and universities. The study used quantitative context-setting surveys and qualitative research to examine the students' information-seeking behaviour, analysing their habits in online and physical research environments and assessing their usage of library and information sources on- and off-line.

The study aimed to establish a benchmark for research behaviour against which subsequent generations of scholars can be measured. Its ultimate aim is to provide guidance to academic institutions, libraries and information specialists on how best to meet the research needs of Generation Y scholars and their immediate successors. The main focus areas of the study were:

• mapping emerging research behaviour trends across the main subject disciplines
investigating how doctoral scholars, in particular those from Generation Y, seek information both on- and off-line
• measuring the relative use of digital resources and physical resources (including research spaces)
• understanding how Generation Y students search for and use digital content for research, and
if and how they use emergent technologies to do so.

Further information on the project may be found on the Education for Change Exploration for Change: Researchers of Tomorrow and the JISC Mapping the needs of a generation webpage.

The UK Data Archive holds data from the three context-setting surveys spanning 2009-2011, but does not currently hold any qualitative materials from the study.


Main Topics:

Across the three years, the surveys covered: personal characteristics; doctoral training; training in and techniques used for finding information and research resources; research, technology and information seeking support; institutional research support; the research process; social media; openness and sharing in research; details regarding doctorate; funding.

Game perimeter: This game is a directory of institutions referenced by L’Onisep which offer higher education. The types of institutions present in this dataset are UFR (Training and Research Units), IUTs (University Institutes of Technology), Engineering and Business Schools, Art Schools and other types of higher education institutions. . Fields of this game: — UAI code (registered administrative unit), — no SIRET, — type of establishment, — name, — abbreviated, — statute (public, private...), — guardianship (ministries, consular chambers...), — University of attachment (labelled and direct access link to the onisep.fr file with unique identifier Onisep) — related establishments (labelled and direct access links to the onisep.fr sheet with unique Onisep identifiers) — geographical coordinates (postal box, postal address, postal code, municipality, common identifier, cedex mention, telephone number, district, department, academy, region, region, longitude X, latitude Y), — open days, — labeling generation 2024 (unexpired labels of the game ‘https://data.education.gouv.fr/explore/dataset/fr-en-etablissements-labellises-generation-2024’ on 25/04/2022). See also: — direct access link to the page onisep.fr and unique identifier Onisep . Updates: This game will be updated approximately ten times a year, at the pace of the orientation calendar and updates to the Onisep.fr site.

Game perimeter: This game is a directory of schools offering secondary education referenced by L’Onisep. The types of schools present in this dataset are colleges, high schools, CFAs (apprentice training centres), structures for the schooling of adapted education (SEGPA, EREA) and the medicosocial sector... . Fields of this game: — UAI code (registered administrative unit), — SIRET No, type of establishment, — name of establishment, — abbreviated, — statute (public, private...), — guardianship (ministries, consular chambers...), — University of attachment (marked and direct link to the file onisep.fr with unique identifier Onisep), — related establishments (labelled and direct access links to the Onisep.fr sheet with unique Onisep identifiers) — geographical coordinates (postal box, postal address, postal code, municipality, common identifier, mention cedex, telephone number, district, department, academy, region, identifying region, longitude X, latitude Y), — open days — languages taught in the institution — labeling generation 2024 (unexpired labels of the game ‘https://data.education.gouv.fr/explore/dataset/fr-en-etablissements-labellises-generation-2024’ on 25/04/2022). See also: — direct access link to the page onisep.fr with unique identifier Onisep. . Updates: This game will be updated approximately ten times a year, at the pace of the orientation calendar and updates to the Onisep.fr site.

The rapid integration of data-intensive, AI-powered technologies in education, often driven by non-EU tech industries, has raised concerns about their social impact, sustainability, and alignment with ethical principles (Rivera-Vargas, 2023; Selwyn, 2023; Williamson, 2023). While the EU has advanced regulatory efforts, such as the AI Act and Ethical Guidelines for AI in Education (Directorate-General for Education, 2022), translating ethical principles into practice remains ambiguous and fragmented (Morley et al., 2023). Despite the proliferation of over 80 ethical frameworks by 2019 (Morley, op.cit), operationalizing these into meaningful educational practices is fraught with ambiguities and challenges. Ethical guidelines are often portrayed as “complementary” tools to mitigate technological risks, yet their transformative potential remains limited (Green, 2021). The funding landscape for projects aimed at fostering knowledge generation, innovation, transformation, and research in the educational sector also encounters significant challenges. The European Union, through funding programs such as Erasmus+ and Horizon Europe, supports educational projects addressing key challenges. Recently, these programs have emphasized the ethical dimension, highlighting the need to align technological and educational advancements with robust principles. However, this focus raises questions about how these values are effectively implemented in practice.

In this context, the present study examines how EU-funded educational projects address ethical principles through a Mixed Methods approach embedding Text-mining and Discourse Analysis. Through a documentary investigation of the Erasmus+ project database, four key searches were conducted, revealing significant gaps. Among the more than 2,000 completed projects, few included ethical reflections on AI or data use, and none explicitly addressed critical issues such as digital sovereignty, platformization, or activism. The initiatives predominantly focused on technical skills (e.g., coding, data analysis), while overlooking critical competencies such as resistance and ethical-political engagement.

Preliminary findings suggest a persistent reliance on techno-solutionist narratives, where ethical guidelines are often reduced to mere compliance checklists, offering minimal transformative value. This misalignment between EU ethical frameworks and project outcomes raises critical concerns regarding the reinforcement of corporate interests and techno-deterministic approaches. The study underscores the necessity of bridging this gap, ensuring that public funding supports socially just, sustainable, and inclusive educational practices. It advocates for funding criteria that emphasize critical perspectives on technology, advancing meaningful agency and systemic transformation beyond superficial ethical commitments (Floridi, 2023).

This record contains:

• The presentation used during the Conference
• The dataset adopted with 3204 EU-Project metadata
• An R script with the preliminary analysis adopted - https://rpubs.com/dcalderon/ECCES_2025">This is also published on RPUBS
• A Python script and the resulting HTML with the creation of an interactive bipartite graph.

References

Directorate-General for Education, Y. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/153756

Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges , and Opportunities. Oxford University Press.

Green, B. (2021). The Contestation of Tech Ethics: A Sociotechnical Approach to Ethics and Technology in Action. http://arxiv.org/abs/2106.01784

Jacovkis, J., Rivera-Vargas, P., Parcerisa, L., & Calderón-Garrido, D. (2022). Resistir, alinear o adherir. Los centros educativos y las familias ante las BigTech y sus plataformas educativas digitales. Edutec. Revista Electrónica de Tecnología Educativa, 82, Article 82. https://doi.org/10.21556/edutec.2022.82.2615

Morley, J., Kinsey, L., Elhalal, A., Garcia, F., Ziosi, M., & Floridi, L. (2023). Operationalising AI ethics: Barriers, enablers and next steps. AI & SOCIETY, 38(1), 411–423. https://doi.org/10.1007/s00146-021-01308-8

Raffaghelli, J. E. (2022). Educators’ data literacy: Understanding the bigger picture. In Learning to Live with Datafication: Educational Case Studies and Initiatives from Across the World (pp. 80–99). Routledge. https://doi.org/10.4324/9781003136842

Rivera-Vargas, C. C., Pablo. (2023). What is ‘algorithmic education’ and why do education institutions need to consolidate new capacities? In The New Digital Education Policy Landscape. Routledge.

Selwyn, N. (2023). Lessons to Be Learnt? Education, Techno-solutionism, and Sustainable Development. In Technology and Sustainable Development. Routledge.

Williamson, B. (2023). The Social life of AI in Education. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-023-00342-5