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Title:Model SAMR in kritična UI pismenost pri pouku matematike
Authors:ID Lipovec, Alenka (Author)
Files:.pdf RAZ_Lipovec_Alenka_2026.pdf (762,61 KB)
MD5: 769EB991F941638C31109709D461A00D
 
URL https://i-pedagogika.si/index.php/ip/article/view/140/461
 
Language:Slovenian
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:PEF - Faculty of Education
Abstract:Prispevek obravnava vključevanje generativne umetne inteligence (Gen-UI) v pouk matematike skozi prizmo modela SAMR ter krepi razumevanje kritične UI pismenosti. Analitični okvir zagotavlja model SAMR, ki zajema nadomestitev, izboljšavo, preoblikovanje in ponovno opredelitev učnih dejavnosti. Pedagoška intervencija je vključevala 59 bodočih učiteljev razrednega pouka, ki so izvedli štiri zaporedne naloge: diferenciacijo naloge, ustvarjanje pesmi, prepoznavanje matematičnih pojavov na slikah in vlogo »Gen-UI kot učenec«. Naloge so bile zasnovane tako, da so postopno višale raven tehnološke integracije ter zahtevale premišljeno oblikovanje pozivov in kritično vrednotenje izhodov Gen-UI. Rezultati so spodbudni: na lestvici z največ 4 točkami je najvišji povprečni dosežek pri nalogi z diferenciacijo (3,36), najnižji pri nalogi »Gen-UI kot učenec« (2,56), kar kaže, da so višje ravni SAMR zahtevnejše, a dosegljive. Kot ključne pomanjkljivosti izstopajo nekritično prevzemanje generiranih rešitev, metodološke napake in premalo specifični pozivi. Na podlagi ugotovitev prispevek oblikuje smernice za smiselno rabo Gen-UI. Avtorji v zaključku priporočajo sistematična usposabljanja o modelu SAMR, oblikovanju kakovostnih pozivov in kritičnem vrednotenju izhodov UI.
Keywords:kritična UI pismenost, pouk matematike, oblikovanje pozivov
Publication status:Published
Publication version:Version of Record
Publication date:10.03.2026
Place of publishing:Maribor
Publisher:Vzgojno-izobraževalni zavod Antona Martina Slomška
Year of publishing:2026
Number of pages:str. 7-20
Numbering:Letn. 2, št. 1
PID:20.500.12556/DKUM-100300 New window
UDC:[37.091.3:51]:004.8
ISSN on article:3024-031X
COBISS.SI-ID:274889475 New window
DOI:10.63069/sxdmrk65 New window
Publication date in DKUM:15.09.2026
Views:194
Downloads:1
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Revija inovativna pedagogika
Publisher:Vzgojno-izobraževalni zavod Antona Martina Slomška
ISSN:3024-031X
COBISS.SI-ID:231494915 New window

Document is financed by a project

Funder:Other - Other funder or multiple funders
Funding programme:Republika Slovenija, Ministrstvo za vzgojo in izobraževanje
Project number:3350-24-3502
Name:Generativna umetna inteligenca v izobraževanju

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:10.03.2026

Secondary language

Language:English
Title:SAMR model and critical AI literacy in mathematics education
Abstract:The paper examines the integration of generative artificial intelligence (GenAI) into mathematics teaching through the SAMR model and strengthens understanding of critical AI literacy. The analysis involves 59 preservice teachers who completed four tasks: task differentiation, song creation, recognition of mathematical phenomena in images, and the “AI as a student” role. Based on their solutions, the paper formulates didactic guidelines for the meaningful use of GenAI. The SAMR model (substitution, augmentation, modification, and redefinition) provides the analytical framework. The pedagogical intervention consisted of four sequential tasks that progressively increased the level of technological integration, requiring careful design and critical evaluation of GenAI outputs. The results are encouraging: on a four-point scale, the highest average score was achieved on the differentiation task (3.36), and the lowest on the “GenAI as a student” task (2.52), indicating that higher SAMR levels are more demanding yet attainable. Key shortcomings included the uncritical adoption of generated solutions, methodological errors, and insufficiently specific prompts. The authors conclude by recommending systematic training on the SAMR model, high-quality prompt design, and critical evaluation of GenAI outputs.
Keywords:critical AI literacy, mathematics education, prompt design


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