000			02932nam a22003257a 4500
003			OSt
005			20250729160334.0
008			250729b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.73 _bC53 2025
100			_aCidro, Janeiah Rhadel T. _eauthor
245			_aExploring the use of ai code generation tools among computer programming students: _bdesign and validation of a cielf-check assessment tool/ _cJaneiah Rhadel T. Cidro, Michaella P. Dado, Vanessa D. Jacinto, Viviene Mae U. Malicdem, and Naobi Czyrus I. Quines.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_axv, 299pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege Of Industrial Education.-- _bBachelor of technical vocational teacher education major in computer programming: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aThe purpose of this study was to investigate how computer programming students used AI code generating tools. To measure students' reliance on AI code generation tool and their level of coding mastery, a CIElf-check evaluation tool was created. With an emphasis on understanding students' attitudes, behaviors, and the educational value of AI code generation tools, the study addressed concerns about how reliance on AI code generation tools may impair critical thinking and problem-solving abilities. This study employed a mixed-methods Design-Based Research (DBR) approach. It determined the influences—attitudes, subjective norms, and perceived behavioral control—that impact students' reliance on AI code generation tools, guided by the Theory of Planned Behavior. The assessment tool's design was motivated by Bloom's Taxonomy, and it was piloted with 68 students, gathering both quantitative and qualitative data for validation. The findings revealed that students valued AI code generation tools for productivity, influenced by peers and industry trends, but expressed concerns about overreliance, ethics, and limited understanding. The CIElf-check tool showed positive impact in coding skills, debugging, and independent learning, with high ratings for engagement (4.19), learning (4.21), and self-directed learning (4.19), all with low variability (SD = 0.7). Key design principles highlighted self-reflection, active engagement, clear feedback, progress tracking, and balanced AI used to support independent coding.
650			_aReliance
650			_aAi code generation tools
650			_aAssessment tool
700			_aDado, Michaella P. _eauthor
700			_aJacinto, Vanessa D. _eauthor
700			_aMalicdem, Viviene Mae U. _eauthor
700			_aQuines, Naobi Czyrus I. _eauthor
942			_2lcc _cBTH CIE _n0
999			_c30552 _d30552