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Compare perceptions and usage patterns of Generative AI among students from different countries.
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Global Student Perspectives on Learning with Generative Artificial Intelligence in Higher Education: A Cross-Cultural Study
Objectives
General objective: to investigate the perceptions, experiences and usage patterns of Generative Artificial Intelligence among higher education students across different cultural contexts.
Identify trends
Identify regional and continental trends in the adoption of Generative AI in higher education.
Examine implications
Examine students' perceptions of the ethical, academic and educational implications of Generative AI.
Research questions
- RQ1
How do higher education students, across different countries and cultural contexts, use Generative AI tools in their academic activities?
- RQ2
What are students' perceptions of the impacts of Generative AI on their learning, including content comprehension, creativity, academic productivity and potential technological dependency?
- RQ3
What ethical concerns do students identify regarding the use of Generative AI in academic contexts, particularly academic integrity, data privacy and information reliability?
- RQ4
How do students perceive the role of higher education institutions in supporting the responsible use of Generative AI, including institutional policies, pedagogical guidance and training?
- RQ5
Are there significant differences across countries, fields of study, academic levels and educational contexts in students' use, perceptions and expectations of Generative AI?
Hypotheses
Formulated to allow comparative, correlational and explanatory analyses between countries and regions, including ANOVA and regression models.
Student profile
- Use and perceptions of Generative AI vary significantly between countries and regions.
- Academic level (undergraduate vs. postgraduate) influences how often students use Generative AI.
- Students in STEM fields use Generative AI more often than students in other fields.
- The mode of study (in-person, hybrid or online) influences the use of Generative AI.
Familiarity, access and use
- More frequent use is associated with a greater diversity of academic applications.
- Institutional or paid access is associated with more frequent use.
- Frequent users tend to use multiple Generative AI tools.
Learning experience
- Use of Generative AI is associated with a perception of better understanding of academic content.
- Use of Generative AI is associated with a perception of greater creativity in learning.
- More frequent use may be associated with a perception of academic dependency.
Institutional support and policies
- Clear institutional policies are associated with higher use of Generative AI.
- Faculty encouragement is associated with a perception that AI use is legitimate in academic contexts.
Ethical concerns
- Students show significant concerns about privacy and data use in Generative AI.
- Limited understanding of how AI works is associated with greater ethical concerns.
Responsible and reflective use
- Higher AI literacy is associated with more critical and responsible use.
- Students who review or adapt AI-generated content show greater academic autonomy.
The future of learning
- Students expect AI-based technologies to have a significant impact on the future of higher education.
- More frequent users tend to see greater future relevance in these technologies.
Open-ended questions
- Open answers will reveal both pedagogical benefits and ethical concerns.
- Perceptions of the opportunities and risks of AI vary between countries and cultures.
Method
Ethics approval
Months 1–2. Data collection starts only after approval by the UNICAMP Research Ethics Committee and, where required, local committees.
Data collection
Months 3–6. An anonymous questionnaire on REDCap, shared by local researchers through official university channels.
Statistical analysis
Months 7–8. Descriptive and inferential analyses, including ANOVA and multiple regression, comparing countries, fields and academic levels.
Writing & dissemination
Months 9–12. Scientific articles, institutional reports and anonymized open datasets in public repositories.
Who can take part
- Students aged 18 or older
- Enrolled in undergraduate or graduate programs at participating institutions
- Voluntary, unpaid participation with electronic informed consent
The questionnaire
- Mostly Likert-scale questions, plus open-ended questions
- Takes about 10 to 15 minutes
- No names, student IDs or emails are collected
Sample
- At least 300 participants per country
- About 35,000 participants if every country reaches the minimum
- Around 14,000 students expected, enough for intercontinental comparisons
Available in 23 languages
English (official)Brazilian PortugueseEuropean PortugueseSpanish (Latin America)Spanish (Europe)FrenchItalianRussianJapaneseChineseKoreanHebrewGreekGermanHindiArabicLatvianTurkishUrduKhmerIndonesianSerbianFilipino
Publications
Scientific articles, regional reports and the Global Report will be published here as results become available. Data analysis and dissemination are planned to begin in April 2027 (Phase 7), with anonymized datasets subsequently made available through recognized open research repositories to support transparency, reproducibility, and further research.
Timeline GlobeGenAI
- 2025
- December 2025
Phase 1: Conception
The project takes shape through the initial development of the research idea, the formation of the core team of lead researchers, and the definition of its global scope, objectives and fundamental research questions.
- 2026
- February 2026
Phase 2: Research Group Name Selection
The research group's name is selected through a democratic voting process, with all members invited to participate and contribute to defining the group's identity.
- March 2026
Phase 3: Research Structure & Ethics
Development and refinement of the research protocols, followed by submission to the relevant Ethics Committees across participating partner institutions worldwide. This phase ensures compliance with international ethical standards and establishes a strong framework for responsible and ethically sound research.
- June 2026
Phase 4: UNICAMP Ethics Approval
Approval from the UNICAMP Research Ethics Committee, ensuring that the study meets the required ethical standards and regulatory requirements for research involving human participants.
- July 2026
Phase 5: Global Expansion, Cultural Adaptation & Ethics
Expansion of the project through the engagement of partner universities across Africa, Asia, Europe, the Americas and Oceania. This phase includes the translation and cultural adaptation of the research instruments, as well as the submission and approval of the study by the relevant Ethics Committees at participating universities, ensuring that the research meets local requirements while maintaining internationally consistent ethical standards.
- October 2026
Phase 6: Data Collection
Active fieldwork begins with the global distribution of the research surveys and the conduct of focus groups with students from diverse cultural, educational and geographic contexts. This phase focuses on gathering rich, cross-cultural data to better understand students' experiences, perceptions and practices regarding Generative AI in higher education.
- 2027
- April 2027
Phase 7: Data Analysis & Publication
Analysis of the collected data using mixed-methods approaches, integrating quantitative and qualitative findings to identify global and regional patterns. This phase also marks the beginning of the preparation and publication of regional reports and the Global Report, highlighting key findings across diverse cultural and educational contexts.