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Applied AI and Machine Learning Journal
Published by Goodwood Publishing
ISSN : -     EISSN : 31249167     DOI : https://doi.org/10.35912/aiml
Core Subject :
Applied AI and Machine Learning Journal (AIML) is a peer-reviewed, open-access scholarly journal dedicated to publishing high-quality original research papers, review articles, and case studies in the fields of artificial intelligence (AI) and machine learning (ML). The journal aims to advance theoretical foundations, innovative methodologies, and real-world applications of intelligent systems that contribute to technological and scientific progress. AIML serves as an interdisciplinary academic platform for academics, researchers, and practitioners to exchange ideas, foster collaboration, and disseminate cutting-edge research findings. The journal covers a broad range of topics, including deep learning, natural language processing, computer vision, robotics, data analytics, and intelligent decision support systems, reflecting the rapidly evolving landscape of AI and ML research. By encouraging global scholarly contributions, Applied AI and Machine Learning Journal (AIML) seeks to promote the ethical, responsible, and sustainable development of artificial intelligence and machine learning technologies. The journal aims to bridge theory and practice by supporting research that delivers meaningful technological innovation and positive societal impact at local, national, and global levels.
Arjuna Subject : -
Articles 12 Documents
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Problem-Solving Skills in Trigonometry as Influenced by Emotional Intelligence and Mathematics Self-Efficacy Jasmin Joyce C. Valer; Mariel L. Alpas; Ronique Byrlle B. Gaborne; Jobelyn C. Macangga; Abdul J. Jamara
Applied AI and Machine Learning Journal Vol 1 No 2 (2026): June
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i2.3963

Abstract

Purpose: This study aimed to determine whether emotional intelligence and mathematics self-efficacy significantly influence the trigonometry problem-solving skills of first- and second-year Bachelor of Secondary Education major in Mathematics students at Davao del Norte State College. Methodology: A descriptive correlational research design was used. A total of 120 students were randomly selected as the respondents. Data were gathered using adapted questionnaires for emotional intelligence and mathematics self-efficacy, along with a researcher-developed test for trigonometry problem-solving skills. Mean, standard deviation, and Spearman’s rho correlation were used for data analysis. Results: The findings revealed that the students demonstrated high levels of emotional intelligence (M = 4.03, SD = 0.46) and mathematics self-efficacy (M = 3.58, SD = 0.52). Their problem-solving performance was also relatively high (M = 35.4, SD = 4.78). However, no significant relationship was found between emotional intelligence and problem-solving skills (r = –0.002, p = 0.979) or between mathematics self-efficacy and problem-solving skills (r = 0.047, p = 0.609). Conclusions: Emotional intelligence and mathematics self-efficacy were not significant predictors of trigonometry problem-solving performance. The findings suggest that cognitive and instructional factors may play a more critical role in students’ mathematical performance. Limitations: The study was limited to first- and second-year mathematics education students from a single institution, used self-reported questionnaires, and a researcher-developed trigonometry test with few items, focusing only on affective variables. Contributions: The study provides evidence that emotional intelligence and mathematics self-efficacy minimally predict trigonometry problem-solving, highlighting the importance of cognitive and instructional factors, and offering guidance for future research and teaching interventions.
Innovative Software-Based Methods for Enhancing Students' Independent Learning in Higher Education Musayev Ashurali Shamshidinovich; Abdusamatova Muslima Abdufattokhoja Qizi
Applied AI and Machine Learning Journal Vol 2 No 1 (2026): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v2i1.4293

Abstract

Purpose: This study examines innovative software-based methods for strengthening students' independent learning in higher education, focusing on digital educational resources, adaptive learning systems, artificial intelligence, virtual laboratories, gamified platforms, and automated assessment tools, and on the institutional conditions that determine whether such tools translate into measurable pedagogical benefit.Methodology: A theoretical and analytical research design was adopted, combining systematic literature analysis with thematicsynthesis of conceptual and empirical studies published mainly between 2021 and 2026, supplemented by regional policy andinstitutional sources relevant to Central Asian higher education. Results: The synthesis shows that integrated digital ecosystems combining adaptive platforms, virtual laboratories, gamifiedinterfaces, learning analytics, and AI-supported assessment substantially strengthen motivation, self-regulation, and academic performance, while infrastructural and digital-competence gaps continue to constrain implementation in transition economies. Conclusions: The pedagogical value of educational software depends less on any single technology than on its coherentintegration within institutional strategy, faculty development, and learner support structures.Limitations: The analytical and secondary-data nature of the study, the absence of primary quantitative testing, and limitedcountry-specific empirical evidence restrict the generalizability of the findings.Contributions: The study offers an integrative conceptual framework linking technological, pedagogical, and institutionaldimensions of software-supported independent learning, with practical implications for policymakers, instructional designers, and university administrators developing digital learning strategies

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