The data set records the perceptions of Bangladeshi university students on the influence that AI tools, especially ChatGPT, have on their academic practices, learning experiences, and problem-solving abilities. The varying role of AI in education, which covers common usage statistics, what AI does to our creative abilities, its impact on our learning, and whether it could invade our privacy. This dataset reveals perspective on how AI tools are changing education in the country and offering valuable information for researchers, educators, policymakers, to understand trends, challenges, and opportunities in the adoption of AI in the academic contex.

Methodology Data Collection Method: Online survey using google from Participants: A total of 3,512 students from various Bangladeshi universities participated. Survey Questions:The survey included questions on demographic information, frequency of AI tool usage, perceived benefits, concerns regarding privacy, and impacts on creativity and learning.

Sampling Technique: Random sampling of university students Data Collection Period: June 2024 to December 2024

Privacy Compliance This dataset has been anonymized to remove any personally identifiable information (PII). It adheres to relevant privacy regulations to ensure the confidentiality of participants.

For further inquiries, please contact: Name: Md Jhirul Islam, Daffodil International University Email: jhirul15-4063@diu.edu.bd Phone: 01316317573

BackgroundGenerative artificial intelligence (Gen-AI) has emerged as a transformative tool in research and education. However, there is a mixed perception about its use. This study assessed the use, perception, prospect, and challenges of Gen-AI use in higher education.MethodsThis is a prospective, cross-sectional survey of university students in the United Kingdom (UK) distributed online between January and April 2024. Demography of participants and their perception of Gen-AI and other AI tools were collected and statistically analyzed to assess the difference in perception between various subgroups.ResultsA total of 136 students responded to the survey of which 59% (80) were male. The majority were aware of Gen-AI and other AI use in academia (61%) with 52% having personal experience of the tools. Grammar correction and idea generation were the two most common tasks of use, with 37% being regular users. Fifty-six percent of respondents agreed that AI gives an academic edge with 40% holding a positive overall perception about the use in academia. Comparatively, there was a statistically significant difference in overall perception between different age ranges (I2 = 27.39; p = 0.002) and levels of education (I2 = 20.07; p 

During a global survey of students conducted in mid-2024, it was found that a whopping 86 percent said they were using artificial intelligence tools in their schoolwork. Almost a fourth of them used it on a daily basis.

This dataset was collected as part of a doctoral research dissertation exploring how generative AI tools support students’ academic writing. The study aimed to examine the profiles of AI use among university students and how variables such as AI self-efficacy, writing self-efficacy, and self-regulated learning predict these profiles and their associated learning outcomes.The data comprises survey responses from 1073 students, who completed a questionnaire measuring constructs such as writing self-efficacy (WSE), AI self-efficacy (ASE), self-regulated learning (SRL), academic grit (AG), writing motivation (WM), perceived improvement (PI), and critical thinking (CT), among others. All responses are anonymized. The dataset also includes aggregated composite variables and calculated profile membership for subsequent quantitative analysis.

This dataset contains the results of a survey conducted on undergraduate students enrolled in the 2nd and 3rd year of study at the Faculty of Cybernetics, Statistics and Economic Informatics. The survey was conducted online and distributed through social media groups. The aim of the survey was to gather insights into students' perceptions of the role of artificial intelligence in education.

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Question 1: On a scale of 1 to 10, how informed do you think you are about the concept of artificial intelligence? (1-not informed at all, 10-extremely informed)

Question 2: What sources do you use to learn about the concept of artificial intelligence? -Internet -Books/Scientific papers (physical/online format) -Social media -Discussions with family/friends -I don't inform myself about AI

Question 3: Express your agreement or disagreement with the following statements: (Strongly Disagree, Partially Disagree, Neutral, Partially Agree, Fully Agree) 1. AI encourages dehumanization 2. Robots will replace people at work 3. AI helps to solve many problems in society (education, agriculture, medicine), managing time and dangerous situations more efficiently 4. AI will rule society

Question 4: Express your agreement or disagreement with the following statements: (Strongly Disagree, Partially Disagree, Neutral, Partially Agree, Fully Agree) 1. Machinery using AI is very expensive and resource intensive to build and maintain 2. AI will lead to a global economic crisis 3. AI will help global economic growth 4. AI leads to job losses

Question 5: When you think about AI do you feel: o Curiosity o Fear o Indifference o Trust

Question 6: In which areas do you think AI would have a big impact? -Education -Medicine -Agriculture -Constructions -Marketing -Public administration -Art

Question 7: On a scale of 1 to 10, how useful do you think AI would be in the educational process? (1- not useful at all, 10-extremely useful)

Question 8: What do you think is the main advantage that AI would have in the teaching process? o Teachers can be assisted by a virtual assistant for teaching lessons and answering students' questions immediately o More efficient management of teachers' time o More interactive and engaging lessons for students o Other

Question 9: What do you think is the main advantage that AI would have in the learning process? o Personalized lessons according to students' needs o Universal access for all students eager to learn, including those with special needs o More interactive and engaging lessons for students o Other

Question 10: What do you think is the main advantage that AI would have in the evaluation process? o Automation of exam grading o Fewer errors in grading system o Constant feedback from virtual assistants for each student o Other

Question 11: What do you think is the main disadvantage that AI would have in the educational process? o Lack of a relationship between students and teacher o Internet addiction o Rarer interactions between students and teachers o Loss of information caused by possible system failure

Question 12: What is your gender? o Female o Male

Question 13: What is your year of study? o Year 2 o Year 3

Question 14: What is your major? o Economic Cybernetics o Statistics and Economic Forecasting o Economic Informatics

Question 15: Did you pass all your exams? o Yes o No

Question 16: What is your GPA for your last year of study? (Note that grades are from 1 to 10 in Romania) o 5.0-5.4 o 5.5.-5.9 o 6.0-6.4 o 6.5-6.9 o 7.0-7.4 o 7.5-7.9 o 8.0-8.4 o 8.5-8.9 o 9.0-9.4 o 9.5-10

The dataset is from an Indian study which made use of ChatGPT- a natural language processing model by OpenAI to design a mental health literacy intervention for college students. Prompt engineering tactics were used to formulate prompts that acted as anchors in the conversations with the AI agent regarding mental health. An intervention lasting for 20 days was designed with sessions of 15-20 minutes on alternative days. Fifty-one students completed pre-test and post-test measures of mental health literacy, mental help-seeking attitude, stigma, mental health self-efficacy, positive and negative experiences, and flourishing in the main study, which were then analyzed using paired t-tests. The results suggest that the intervention is effective among college students as statistically significant changes were noted in mental health literacy and mental health self-efficacy scores. The study affirms the practicality, acceptance, and initial indications of AI-driven methods in advancing mental health literacy and suggests the promising prospects of innovative platforms such as ChatGPT within the field of applied positive psychology.: Codebook for the Dataset provided

With the growing prevalence of AI tools, such as ChatGPT, political science instructors are navigating how to manage the use and misuse of AI in the classroom. This study underscores the prevalence of AI in academic settings and suggests pedagogical practices to integrate AI in the classroom in ways informed by students’ current interactions with and attitudes toward AI. Using a survey of undergraduate students in political science courses, the study finds both that ChatGPT usage is widespread at the university level, but also that students are not confident in their skills for using AI appropriately to help improve their writing or prepare for exams. These findings point to key areas where instructors can intervene and integrate AI in ways that enhance student learning, reduce potential achievement gaps that may emerge due to differences in AI usage across student background, and help students develop critical AI literacy skills to prepare for careers that are increasingly affected by AI.

Analysis of ‘U.S. News and World Report’s College Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/flyingwombat/us-news-and-world-reports-college-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Statistics for a large number of US Colleges from the 1995 issue of US News and World Report.

Content

A data frame with 777 observations on the following 18 variables.

Private A factor with levels No and Yes indicating private or public university

Apps Number of applications received

Accept Number of applications accepted

Enroll Number of new students enrolled

Top10perc Pct. new students from top 10% of H.S. class

Top25perc Pct. new students from top 25% of H.S. class

F.Undergrad Number of fulltime undergraduates

P.Undergrad Number of parttime undergraduates

Outstate Out-of-state tuition

Room.Board Room and board costs

Books Estimated book costs

Personal Estimated personal spending

PhD Pct. of faculty with Ph.D.’s

Terminal Pct. of faculty with terminal degree

S.F.Ratio Student/faculty ratio

perc.alumni Pct. alumni who donate

Expend Instructional expenditure per student

Grad.Rate Graduation rate

Source

This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

The dataset was used in the ASA Statistical Graphics Section’s 1995 Data Analysis Exposition.

--- Original source retains full ownership of the source dataset ---

The data here is from the report entitled Trends in Enrollment, Credit Attainment, and Remediation at Connecticut Public Universities and Community Colleges: Results from P20WIN for the High School Graduating Classes of 2010 through 2016.

The report answers three questions: 1. Enrollment: What percentage of the graduating class enrolled in a Connecticut public university or community college (UCONN, the four Connecticut State Universities, and 12 Connecticut community colleges) within 16 months of graduation? 2. Credit Attainment: What percentage of those who enrolled in a Connecticut public university or community college within 16 months of graduation earned at least one year’s worth of credits (24 or more) within two years of enrollment? 3. Remediation: What percentage of those who enrolled in one of the four Connecticut State Universities or one of the 12 community colleges within 16 months of graduation took a remedial course within two years of enrollment?

Notes on the data: CT Remed: % Enrolled in Remediation is a subset of the % Enrolled in 16 Months.

In this this work, a study is presented with quantitative variables using machine learning tools to detect undergraduate students at risk of dropping out and the factors associated with this behavior. Clustering algorithms and classification methods were tested to determine the predictive power of several variables regarding the dropout phenomenon on an unbalanced database of 14,495 undergraduate students with a real dropout rate of 8.5% and a retention rate of 91.5%. The usual classification criterion that assigns individuals to a class if their probability of belonging to it is greater than 50% provided accuracies of 13.2% in the dropout prediction and 99.4% in the retention prediction. Among eight classifiers, Random Forest was selected and applied along with Threshold Probability, which allowed us to gradually increase the dropout precision to more than 50%, while maintaining retention and global precisions above 70%. Through this study, it was found that the main variables associated with student dropouts were their academic performance during the early weeks of the first semester, their average grade in the previous academic levels, the previous mathematics score, and the entrance exam score. Other important variables were the number of class hours being taken, student age, funding status of scholarships, English level, and the number of dropped subjects in the early weeks. Given the trade-off between dropout and retention precisions, our results can guide educational institutions to focus on the most appropriate academic support strategies to help students at real risk of dropping out.

Alesco's college and alumni data contains demographic information on almost every college student in the nation. Nowhere else will you find more complete and accurate information on student and alumni, individuals by name and age and career interests along with detailed financial-related data including income.

Our student data is built by utilizing hundreds of sources including public records, directories, county recorder and tax assessor files, US Census data, surveys, and purchase transactions. We continuously utilize USPS processing routines to give you the most complete and up-to-date addresses.

Flexible pricing available to meet all your business needs. Student Data is available on a transactional basis or unlimited use cases for marketing and analytics.

Student Video Interaction Dataset 🎥📊 Overview This dataset captures real-world student interactions with educational videos. It is designed to help analyze engagement levels, predict best-performing videos, and enhance AI-driven recommendation systems.

Dataset Features The dataset includes detailed metadata about each video and student engagement metrics:

Column Name Description Title Title of the video Duration in Seconds Length of the video in seconds Tags Keywords associated with the video content Topic ID Unique identifier for the topic Topic Name Name of the topic covered in the video Likes Number of likes the video received Views Total number of views Engagement Score between 1-10, indicating student interaction level Result Performance score between 50-100, representing video effectiveness Video Link URL to the video Use Cases & Applications 🚀 🔹 Educational Analytics: Identify the most engaging videos for different topics. 🔹 AI-Powered Recommendations: Train machine learning models to recommend the best videos to students. 🔹 Behavioral Insights: Understand student engagement patterns based on interaction data. 🔹 LSTM-Based Predictions: Use sequential modeling to predict future engagement trends.

Potential Machine Learning Tasks 🤖 ✔ Engagement Prediction – Predict how engaging a video will be based on past student interactions. ✔ Personalized Recommendations – Develop AI models to suggest relevant educational content. ✔ Trend Analysis – Identify which topics perform best over time. ✔ A/B Testing for Video Optimization – Compare different video formats to improve engagement.

Analysis of ‘State University of New York (SUNY) - NYS High School Attended by First Time Undergraduate Students: Beginning Fall 2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/245345ad-21d9-43cf-b820-0cc771ae12be on 12 February 2022.

--- Dataset description provided by original source is as follows ---

Report by NYS High School of the number of SUNY First Time Undergraduates entering in the Term, who reported attending that High School. The total number of First Time Undergraduates at SUNY from each High School is provided. The total is sub-divided by the SUNY Sector of the Institution the student is attending; Doctoral, Comprehensive, Technology and Community College. The report also provides a total count by NYS County, which is an aggregate of all the high schools in that County.

--- Original source retains full ownership of the source dataset ---

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

Report by NYS High School of the number of SUNY First Time Undergraduates entering in the Term, who reported attending that High School. The total number of First Time Undergraduates at SUNY from each High School is provided. The total is sub-divided by the SUNY Sector of the Institution the student is attending; Doctoral, Comprehensive, Technology and Community College. The report also provides a total count by NYS County, which is an aggregate of all the high schools in that County.

Analysis of ‘SAT (College Board) 2010 School Level Results’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/37a58362-4cfc-4c4e-a026-c83036afc11a on 26 January 2022.

--- Dataset description provided by original source is as follows ---

New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores.

Records with 5 or fewer students are suppressed (marked ‘s’).

College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report.

Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school.

Data collected and processed by the College Board.

--- Original source retains full ownership of the source dataset ---

This dataset explores the neurobehavioral factors related to eating disorders among university students in Bangladesh. The survey was distributed across various universities in Bangladesh via email and social media platforms. A total of 550 responses were collected between August and November 2024, with 296 students expressing a positive perception of eating disorders and 254 students expressing a negative perception. The dataset covers seven key factors: demographic, cognitive control, affective value representation, salience or taste processing, body image perception, reward processing or habit formation, and self-perception about eating disorders. This dataset offers valuable insights for researchers investigating the neurobiological underpinnings of eating disorders, supporting the development of brain-based models to better understand and address these conditions.

Analysis of ‘ Higher Education Students Performance Evaluation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/csafrit2/higher-education-students-performance-evaluation on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Abstract

The data was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019. The purpose is to predict students' end-of-term performances using ML techniques.

Attribute Information:

Student ID 1- Student Age (1: 18-21, 2: 22-25, 3: above 26) 2- Sex (1: female, 2: male) 3- Graduated high-school type: (1: private, 2: state, 3: other) 4- Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full) 5- Additional work: (1: Yes, 2: No) 6- Regular artistic or sports activity: (1: Yes, 2: No) 7- Do you have a partner: (1: Yes, 2: No) 8- Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410) 9- Transportation to the university: (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other) 10- Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other) 11- Mother's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.) 12- Father's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.) 13- Number of sisters/brothers (if available): (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above) 14- Parental status: (1: married, 2: divorced, 3: died - one of them or both) ***Listed as "Kids"...woops 15- Mother's occupation: (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other) 16- Father's occupation: (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other) 17- Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours) 18- Reading frequency (non-scientific books/journals): (1: None, 2: Sometimes, 3: Often) 19- Reading frequency (scientific books/journals): (1: None, 2: Sometimes, 3: Often) 20- Attendance to the seminars/conferences related to the department: (1: Yes, 2: No) 21- Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral) 22- Attendance to classes (1: always, 2: sometimes, 3: never) 23- Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable) 24- Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never) 25- Taking notes in classes: (1: never, 2: sometimes, 3: always) 26- Listening in classes: (1: never, 2: sometimes, 3: always) 27- Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always) 28- Flip-classroom: (1: not useful, 2: useful, 3: not applicable) 29- Cumulative grade point average in the last semester (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49) 30- Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49) 31- Course ID 32- OUTPUT Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA)

Acknowledgements

Relevant Papers:

Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham.

Citation Request:

Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham.

Inspiration

Which students are most likely to succeed?

--- Original source retains full ownership of the source dataset ---

Decrease the percentage of students enrolled in remedial coursework in college from 39.38% in 2014 to 35% by 2018.

Analysis of ‘CollegeEnrollment12m 20162017 byTract 20190722’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/df1a5e60-59b6-4b55-acd3-1609aa1ab777 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains college enrollment information, by census tract, for the state of Michigan. College enrollment was defined as the number of public high school students who graduated in 2017, who enrolled in a college or university within 12 months of their high school graduation. This dataset includes enrollment in two-year and four-year institutions of higher education.

--- Original source retains full ownership of the source dataset ---