MANAGING THE INTERSECTION OF ARTIFICIAL INTELLIGENCE, DIGITAL TYPING, AND HANDWRITING FOR SUSTAINABLE QUALITY EDUCATION ENHANCEMENT
Bibliographic record
Abstract
Objective: The research objective is to investigate how the rapid advancement of technology, particularly the increasing reliance on digital tools and artificial intelligence (AI) for note-taking and communication, influences handwriting practices. Specifically, to explore the attitudes, emotions, and experiences surrounding handwriting and digital communication in Jakarta. Theoretical Framework: This research employs Social Change Theory as the theoretical framework to understand how technology influences handwriting practices. Social Change Theory provides a lens through which to examine the societal shifts brought about by technological advancements, considering how these changes impact individuals' behaviors, attitudes, and perceptions regarding handwriting and digital communication. Method: The study utilizes qualitative research methods, including focus group discussions, observations, and interviews. These methods allow for a comprehensive exploration of participants' attitudes, emotions, and experiences related to handwriting and digital communication. Results and Discussion: Findings from the study suggest that while AI and digital typing offer efficiency and accessibility, handwriting retains unique cognitive and emotional benefits. Participants express a range of attitudes towards handwriting and digital communication, highlighting both the advantages and drawbacks of each method. Managing the integration of AI, digital typing, and handwriting in education emerges as a potential solution to address concerns about technology dependence while fostering critical thinking and cultural appreciation for sustainability and the enhancement of the quality of education, as SDG’ No. 4. Research Implications: The findings of this research have several implications for educational practice and policy that should recognize the value of incorporating handwriting alongside AI and digital typing in educational curricula.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".