Artificial intelligence in Ethiopian school curriculum: Educators' practices, challenges, and recommendations

被引:0
作者
Deriba, Fitsum Gizachew [1 ]
Sanusi, Ismaila Temitayo [1 ]
机构
[1] Univ Eastern Finland, Sch Comp, POB 111, Joensuu 80101, Finland
来源
COMPUTERS AND EDUCATION OPEN | 2025年 / 8卷
关键词
Artificial intelligence; School education; Teachers; Motivation; Ethiopia; MOTIVATION; TEACHERS;
D O I
10.1016/j.caeo.2025.100251
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In light of the ongoing discourse on integrating artificial intelligence (AI) into formal education systems, it is imperative to examine both curriculum and teaching practices, particularly in developing regions. This study explores the Information Communication and Technology (ICT) curriculum of Ethiopian secondary schools to identify AI-related content within the learning guide. Additionally, it assesses ICT educators' knowledge, practices, challenges, and recommendations for effectively implementing AI in Ethiopian schools. Employing a qualitative approach, this study reviewed AI content in the Ethiopian secondary school ICT curriculum and conducted audio-recorded interviews with 10 ICT teachers. Document analysis and thematic analysis were utilized to interpret the collected data. The AI content in the curriculum was analyzed and findings were discussed within the framework of the Five Big Ideas in AI. Our findings reveal that the AI content in the Ethiopian secondary school ICT curriculum is predominantly definitional. The thematic analysis of teacher interview data highlights the methods employed in teaching AI, including pedagogical challenges such as limited understanding of AI concepts. Furthermore, several recommendations emerged for the effective implementation of AI in schools, including curriculum revisions to incorporate AI topics in early childhood and primary education, as well as professional development opportunities. We also discuss the implications, limitations, and future research directions of this study.
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页数:16
相关论文
共 73 条
  • [1] Adu P., 2019, A step-by-step guide to qualitative data coding, DOI DOI 10.4324/9781351044516
  • [2] AI4K12, The Artificial Intelligence (AI) for K-12 initiative (AI4K12)
  • [3] Towards a Framework for the Analysis of Multi-Product Lines in the Automotive Domain
    Ali, Shaukat
    Arcaini, Paolo
    Hasuo, Ichiro
    Ishikawa, Fuyuki
    Lee, Nian-Ze
    [J]. PROCEEDINGS OF THE 13TH INTERNATIONAL WORKSHOP ON VARIABILITY MODELLING OF SOFTWARE-INTENSIVE SYSTEMS (VAMOS '19), 2019,
  • [4] [Anonymous], 2022, K-12 AI curricula: A mapping of government-endorsed AI curricula
  • [5] Teachers' motivation to learn: implications for supporting professional growth
    Appova, Aina
    Arbaugh, Fran
    [J]. PROFESSIONAL DEVELOPMENT IN EDUCATION, 2018, 44 (01) : 5 - 21
  • [6] Avenier M.-J., 2015, French Journal of Management Information Systems, V20, DOI DOI 10.9876/SIM.V20I1.632
  • [7] Evaluating integrated use of information technologies in secondary schools of Ethiopia usingdesign-realitygap analysis: A school-level study
    Bati, Tesfaye Bayu
    Workneh, Anteneh Wasyhun
    [J]. ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES, 2021, 87 (01):
  • [8] Inventing Artificial Intelligence in Ethiopia
    Blackwell, Alan F.
    Damena, Addisu
    Tegegne, Tesfa
    [J]. INTERDISCIPLINARY SCIENCE REVIEWS, 2021, 46 (03) : 363 - 385
  • [9] One size fits all? What counts as quality practice in (reflexive) thematic analysis?
    Braun, Virginia
    Clarke, Victoria
    [J]. QUALITATIVE RESEARCH IN PSYCHOLOGY, 2021, 18 (03) : 328 - 352
  • [10] Ethical Considerations in Artificial Intelligence Courses
    Burton, Emanuelle
    Goldsmith, Judy
    Koenig, Sven
    Kuipers, Benjamin
    Mattei, Nicholas
    Walsh, Toby
    [J]. AI MAGAZINE, 2017, 38 (02) : 22 - 34