Evidence map›Paper›PMID 36844361›Full record

ArticleEducational technology research and development : ETR & D2023

Teachers' AI digital competencies and twenty-first century skills in the post-pandemic world.

Davy Tsz Kit Ng, Jac Ka Lok Leung, Jiahong Su, Ross Chi Wui Ng, Samuel Kai Wah Chu

Abstract read
In one paragraph

Article in Educational technology research and development : ETR & D, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Davy Tsz Kit NgFaculty of Education, The University of Hong Kong, Hong Kong, China.ORCID 0000-0002-2380-7814
Jac Ka Lok LeungDivision of Integrative Systems and Design, The Hong Kong University of Science and Technology, Hong Kong, China.
Jiahong SuFaculty of Education, The University of Hong Kong, Hong Kong, China.
Ross Chi Wui NgFaculty of Education, The University of Hong Kong, Hong Kong, China.
Samuel Kai Wah ChuFaculty of Education, The University of Hong Kong, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pandemic has catalyzed a significant shift to online/blended teaching and learning where teachers apply emerging technologies to enhance their students' learning outcomes. Artificial intelligence (AI) technology has gained its popularity in online learning environments during the pandemic to assist students' learning. However, many of these AI tools are new to teachers. They may not have rich technical knowledge to use AI educational applications to facilitate their teaching, not to mention developing students' AI digital capabilities. As such, there is a growing need for teachers to equip themselves with adequate digital competencies so as to use and teach AI in their teaching environments. There are few existing frameworks informing teachers of necessary AI competencies. This study first explores the opportunities and challenges of employing AI systems and how they can enhance teaching, learning and assessment. Then, aligning with generic digital competency frameworks, the DigCompEdu framework and P21's framework for twenty-first century learning were adapted and revised to accommodate AI technologies. Recommendations are proposed to support educators and researchers to promote AI education in their classrooms and academia.

Indexed as

AI educationAI literacyDigital competencyTeacherTwenty-first century skills

Identifiers

PMID36844361
PMCPMC9943036

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.