Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-2090
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T10:41:35.865914Z
status: published
lens: p_space
sector: education
headline: Assessment Design Needs: A Preliminary Study on Using Generative Artificial Intelligence Tools for Educators
dek: Assessment is considered as a key feature and central pillar in the teaching and learning process, particularly in making of judgement and measurement of student achievement and mastery of learning outcomes. The traditional methods of designing assessments items face several challenges, such as the need and requirement to design the assessment items manually, the time-consuming nature of the design process and the required cognitive skills through formal training provided by various institutions. As the result,…
gain_title: (none)
problem_title: Educators in a 46-person preliminary survey showed low generative AI and prompt engineering skills, and found assessment test items and practical tests among the hardest tasks to design.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Educators in a 46-person preliminary survey showed low generative AI and prompt engineering skills, and found assessment test items and practical tests among the hardest tasks to design.
problem_evidence: most of the respondents have lower level of using GAI tools and prompt engineering skills | educators lack skills in designing assessment materials using GAI
quick_read: A preliminary analysis-phase study surveyed forty-six educators about their understanding of Outcome-Based Education, assessment literacy, and generative AI literacy. It found that most respondents had low levels of generative AI tool use and prompt engineering skill.

Use was concentrated in general teaching and learning and information searching, while assessment design was where educators struggled most. This matters because traditional manual assessment design is slow and can yield items lacking validity, reliability and consistent scoring. The sample is small, and the study does not test whether AI tools actually improve assessment quality.
limitation: The study is described as preliminary, covering only the analysis phase with forty-six educators, so findings may not generalize and do not show effects of AI-supported assessment.
tag: Evidence-backed problem
key_points: Forty-six educators took part in the preliminary study. | 43.5% reported using generative AI for teaching and learning, and 34.8% for searching information. | Writing assessment test items was the most difficult task at 28.3%, followed by practical tests at 21.7%. | Presentations were cited as difficult by 17.4% and projects by 10.9%. | The authors propose a standardized guideline to incorporate AI ethically in assessment.
rundown: The study sets out from the view that assessment is a central pillar of teaching and learning, used to judge student achievement and mastery of learning outcomes. Traditional item design is manual, time-consuming, and requires formal training, which can leave items lacking context-specific validity and reliability.

Survey results showed other uses were small: idea collection at 6.5%, reflection or journaling at 4.3%, writing programming code at 4.3%, image generation at 4.3%, and other tasks at 2.2%. Simulations (6.5%) and fieldwork (5.4%) were the least frequently cited difficult tasks.

The authors conclude that educators lack skills in designing assessment materials with generative AI. They suggest a guideline as an innovative assessment tool that would standardize practice and support ethical use of AI.
sources:
- peer_reviewed | Pertanika Journal of Artificial Intelligence | https://doi.org/10.47836/pjai.1.1.02 | 2026-10-21
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

4a48ffac239b17fceb5e816c8abf1846aabab3e566305302cdf24835c869c037

previous

0000000000000000000000000000000000000000000000000000000000000000