schema · 21 fields
Assessing the Level of AI Literacy Among University Students: A cross-national study
Automate extraction for "Assessing the Level of AI Literacy Among University Students: A cross-national study ". Extract structured, evidence-grounded data points directly from hundreds of papers with source page coordinates.
Schema Field Definitions (21)
| Field Name | Data Type | Description |
|---|---|---|
| study_country | string | The country where the study was primarily conducted. Use the country name (e.g., 'United States', 'Japan'). |
| university_type | string | The classification of the participating institution. Must be one of: Public, Private, Technical Institute, Mixed. |
| total_sample_size | number | The total number of students surveyed or assessed, as an integer. |
| academic_level | string | The general academic level of the students surveyed. Must be one of: Undergraduate, Postgraduate, Doctoral, Mixed. |
| field_of_study | string | The dominant field of study for the cohort. Must be one of: STEM, Humanities, Social Sciences, Business/Economics, Arts, Education, Mixed. |
| mean_age | number | The average age of the participants, as a floating-point number. |
| mean_age_unit | string | The unit for the mean_age value. Must be 'years'. |
| percentage_female | number | The percentage of participants identifying as female (0-100), as a floating-point number. |
| data_collection_method | string | The primary method used for data collection. Must be one of: Online Survey, Paper-based Survey, Interviews, Mixed Method. |
| ai_literacy_scale_name | string | The name of the assessment instrument or scale used to measure AI literacy. Example: 'AI Literacy Assessment (AILA)'. |
| maximum_possible_score | number | The maximum attainable score on the AI literacy scale, as an integer or floating-point number. |
| mean_ai_literacy_score | number | The average overall AI literacy score obtained by the student sample, as a floating-point number. |
| mean_ai_literacy_score_unit | string | The unit for the mean AI literacy score. Use 'points' or 'percentage' or 'scale score'. |
| ai_knowledge_subscore | number | The reported mean score specifically related to foundational AI knowledge/concepts, if measured separately, as a floating-point number. |
| ai_knowledge_subscore_unit | string | The unit for the AI knowledge subscore. Use 'points' or 'scale score'. |
| ai_ethics_subscore | number | The reported mean score specifically related to AI ethics and societal impact, if measured separately, as a floating-point number. |
| ai_ethics_subscore_unit | string | The unit for the AI ethics subscore. Use 'points' or 'scale score'. |
| perceived_competence_level | string | The reported self-assessment level of competence or confidence with AI tools. Must be one of: High, Moderate, Low, Not Reported. |
| utilized_ai_tools_regularly | boolean | Did the study report that a majority (>=50%) of participants regularly utilize AI tools (e.g., ChatGPT, Copilot)? Must be true or false. |
| correlation_with_gpa_reported | boolean | Was the correlation between AI literacy score and GPA/academic performance reported? Must be true or false. |
| doi | string | The Digital Object Identifier (DOI) or a full bibliographic reference for the source paper. |
Data Verification Status Taxonomy
To maintain rigorous scientific standards, Sci-database differentiates between automated signals and human audit:
Extracted
Value located and extracted from the full-text PDF by the Kateeb engine.
Model-Agreed
An independent second model produced a compatible value during automated second-pass review.
Human-Reviewed
A named researcher audited the value against the visual page coordinates.
Benchmark-Validated
Performance evaluated against a standardized, published evaluation protocol.
Replicated
An independent external research group reproduced the extraction and experimental result.