Taxonomy of Educational Objectives: Teaching, Learning, and Assessing in the Information and Artificial Intelligence Era
Bibliographic record
Abstract
This study reviews existing taxonomies and proposes a new educational taxonomy that fulfills the educational needs of the current era, the information and Artificial Intelligence (AI) era. The review of previous educational taxonomies revealed that although they provide insights into establishing educational objectives and learning outcomes, they still need to address recent changes and challenges in learning processes. To (1) integrate the new realities into the landscape of learning (i.e., Education for sustainable development (ESD), soft skills development, and AI), (2) maintain the classroom as the formal venue for learning, and (3) strengthen the position and role of instructors as facilitators, a new six-category two-fold hierarchy-based taxonomy is proposed (AlAfnan Taxonomy): (1) Knowledge and Comprehension, (2) Synthesis and Evaluation, (3) Ethical and Moral Reasoning, (4) Application and Strategic Thinking, (5) Creativity and Innovation, and (6) Lifelong Learning and Adaptability. The taxonomy begins with foundational levels of ‘Knowledge and Comprehension’ stressing the importance of understanding fundamental realities and concepts within specific fields. Then, it addresses the importance of ‘Synthesis and Evaluation’ as essential and crucial skills for navigating an information-rich world. ‘Ethical and Moral Reasoning’ highlights the significance of ethical decision-making, moral frameworks, and culture-based diversity. Further, the taxonomy introduces ‘Application and Strategic Thinking’, emphasizing the practical use of knowledge in real-world scenarios and the ability to devise long-term plans. ‘Creativity and Innovation’ are essential drivers of progress in an era characterized by rapid technological advancements encouraging learners to explore novel solutions and approaches. Lastly, ‘Lifelong Learning and Adaptability’ underscores the necessity of continuous learning and flexibility in response to evolving circumstances, ensuring students and graduates remain competitive and relevant throughout their lives. By nurturing a multifaceted skill set encompassing critical thinking, ethical awareness, practical application, creativity, and adaptability, this taxonomy aims to equip learners with the necessary tools to excel in a dynamic and complex world, making it indispensable for modern education.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".