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All Journal Indonesian Journal of Pedagogy and Teacher Education Journal of Early Childhood Development and Education International Journal of Advances in Artificial Intelligence and Machine Learning International Journal of Sustainable Business, Management and Accounting
Amel Zulfukar Hassan Adlan
Nile Valley University
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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The Role of Artificial Intelligence in Enhancing Sustainable Accounting and ESG Reporting: A Systematic Literature Review Eko Risdianto; Amel Zulfukar Hassan Adlan; Usmanova Shokhsanam Avazovna
International Journal of Sustainable Business, Management and Accounting Vol. 2 No. 1 (2026): International Journal of Sustainable Business, Management, and Accounting (IJSB
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijsbma.v2i1.170

Abstract

Background of study: The increasing demand for sustainability and transparency has strengthened the role of sustainable accounting and Environmental, Social, and Governance (ESG) reporting in modern business practices. However, traditional reporting systems face significant challenges in managing complex, large-scale, and heterogeneous ESG data, leading to limitations in accuracy, consistency, and timeliness. Although artificial intelligence (AI) has been widely adopted in financial analysis, its application in sustainable accounting and ESG reporting remains fragmented and underexplored. Aims: This study aims to provide a comprehensive and structured analysis of the role of AI in enhancing sustainable accounting and ESG reporting. Methods: This study employs a Systematic Literature Review (SLR) using the PRISMA framework, combined with bibliometric analysis. Data were collected from the Scopus database using three keyword strategies and filtered based on predefined inclusion criteria. The dataset was cleaned using OpenRefine and analyzed using VOSviewer and Biblioshiny to explore research trends, thematic structures, and intellectual development. Result: The results show that AI technologies, particularly machine learning, natural language processing, and big data analytics, significantly improve ESG data processing, reporting efficiency, and predictive decision-making. However, ESG reporting practices remain fragmented, lack standardization, and are often implemented in isolated contexts. Conclusion: This study contributes by integrating AI, sustainable accounting, and ESG reporting into a unified perspective supported by systematic and bibliometric analysis. The findings highlight the potential of AI to enhance transparency and efficiency in sustainability reporting, while emphasizing the need for integrative frameworks and empirical validation in future research.
Failure Mode Analysis of Machine Learning Models in Realistic Data Deployment Scenarios Lau Meng Cheng; Amel Zulfukar Hassan Adlan
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.651

Abstract

Background: Machine learning models frequently demonstrate strong performance under controlled benchmark evaluations. However, such evaluations often fail to capture hidden vulnerabilities that emerge under realistic deployment conditions. In real-world environments, models are exposed to stressors such as label corruption, feature noise, distributional shifts, and operational constraints, including reduced computational precision and increased latency. These conditions can induce performance degradation and structural instability, highlighting the need for a systematic robustness evaluation framework that goes beyond conventional accuracy metrics.Aims: This paper aims to introduce a formalized Failure Mode Analysis Protocol (FMAP) for evaluating machine learning model robustness under realistic operational stressors. The study reconceptualizes robustness evaluation as a distribution-based process, where model deployment itself generates a new distribution over time.Methods: The proposed FMAP framework evaluates model behavior under progressively adverse conditions, including symmetric label corruption, additive feature noise, distributional shifts, and operational constraints such as reduced numerical precision and increased inference latency. Experiments were conducted across diverse tabular and image benchmark datasets using representative model architectures, including linear models, ensemble methods, margin-based models, and deep neural networks.Result: The experiments reveal distinct robustness profiles across model architectures when exposed to escalating stress conditions. Operational constraints and compositional limitations were shown to induce measurable degradation patterns, including instability and output collapse under extreme stress. The findings demonstrate that model failure is not solely a function of predictive accuracy loss but is closely linked to operational constraints and evolving distributional conditions. The distribution-based evaluation framework effectively captures early-stage degradation and full failure transitions.Conclusion: This study establishes a structured protocol for analyzing machine learning failure modes under realistic deployment scenarios. By framing robustness evaluation as a distribution-based process, the FMAP approach provides a systematic method for identifying operational risks and structural vulnerabilities.
Language Learning and Emotional Growth: A Study of Preschool Children in Multilingual Contexts – Sudan Amel Zulfukar Hassan Adlan; Khabir Othman Mohamed Badawi; Rose Chikopela
Journal of Early Childhood Development and Education Vol. 3 No. 2 (2026): Journal of Early Childhood Development and Education (May)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/junior.v3i2.694

Abstract

Background: The development of young children is greatly dependent on language exposure, but the relationship between multilingualism and socio-emotional development remains relatively unexplored with regard to Sudanese culture. Multilingualism in the Sudanese culture involves a combination of Arabic, English, and dialect, which can work as a tool to foster cognitive and emotional resilience in early childhood. Objective: This paper seeks to establish the role of multilingual settings in the development of cognitive flexibility and socio-emotional intelligence in preschoolers (3–5 years old) enrolled in the Noor Albayan Kindergarten in Atbara, Sudan. Methods: Through the use of convergent parallel mixed methods research design, the study conducted a quantitative assessment of 25 children ($N=25$) using Sort-Switch tasks. The quantitative data obtained were subjected to analysis through one sample t-tests ($df=24$). On the other hand, qualitative data were obtained by conducting semi-structured interviews involving 10-12 key informants (teachers and parents). Results: Analysis of the quantitative data showed that multilingual children scored 72% on problem-solving tasks ($t (24) =9.17, p<.001$). Additionally, they scored 68% on social adaptation skills ($t (24) =6.43, p<.005$) and 65% on emotional awareness tasks ($t (24) =5.00, p<.005$). Moreover, qualitative data showed. Conclusion: These results indicate that bilingualism speeds up the development of prefrontal cortex and emotional differentiation. The findings of this paper recommend the application of translanguaging practices in Sudanese Early Childhood Education (ECE).
Exploring Vocabulary Learning Strategies in EFL: A Case Study of Sudanese University Students Amel Zulfukar Hassan Adlan; Eko Risdianto
Indonesian Journal of Pedagogy and Teacher Education Vol. 4 No. 2 (2026): Indonesian Journal of Pedagogy and Teacher Education (Special Issue August 2026
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijopate.v4i2.678

Abstract

Background: Sudanese University students face significant challenges in English proficiency due to historical Arabicization policies and outdated pedagogical frameworks. While vocabulary is a primary predictor of linguistic success, a systemic gap exists between strategy awareness and long-term lexical retention. Aims: This study investigated the relationship between Vocabulary Learning Strategies (VLS), institutional support, and actual proficiency (receptive and productive) among Sudanese undergraduates. Methods: Using s triangulated quantitative and documentary design ($N=240$), the study utilized the Vocabulary Levels Test (VLT), Lex30, a Likert-scale VLS questionnaire, and a systematic analysis of university syllabi. Data were analyzed using Structural Equation Modelling (SEM) and psychometric validation. Results: Psychometric tools demonstrated strong internal validity ($\text{VLT } \alpha = 0.86$; Lex30 $M = 14.2/30$). Latent profile analysis revealed a severe "Knowledge-Application Gap": 64% of students possess theoretical awareness of exploratory strategies, yet 30% of the total sample occupy an "Overlap Group" experiencing persistent lexical acquisition failures. SEM confirmed that while discovery strategies predict receptive ($\beta = 0.35$) and productive ($\beta = 0.29$) outcomes, consolidation strategies are the strongest predictors of productive mastery ($\beta = 0.41$). Crucially, institutional support moderates strategy effectiveness by 18% ($\beta = 0.18$). Documentary analysis revealed that 83% of syllabi ignore explicit strategy instruction entirely, and 0% feature systematic spaced-retrieval tasks. Conclusion: The findings demonstrate that individual learner strategic awareness is heavily suppressed by a systemic instructional vacuum. Sudanese university curricula are overwhelmingly biased toward passive word recognition, failing to provide the structured consolidation routines required to transition vocabulary from short-term memory into active communicative competence. Rectifying this deep-seated proficiency gap requires immediate curriculum reform, institutionalized strategy modeling, and the integration of low-bandwidth, offline-capable digital learning tools.  
Co-Authors Eko Risdianto Khabir Othman Mohamed Badawi Lau Meng Cheng Rose Chikopela Usmanova Shokhsanam Avazovna