[et_pb_section fb_built=”1″ theme_builder_area=”post_content” _builder_version=”4.27.4″ _module_preset=”default”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” column_structure=”1_2,1_2″ theme_builder_area=”post_content”][et_pb_column _builder_version=”4.27.4″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” theme_builder_area=”post_content” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n
The Faculty of Science, Universiti Teknologi Malaysia (UTM)<\/strong> continues to lead innovation in digital education with the development of AiEdu<\/strong>, an artificial intelligence (AI) framework designed to evaluate student performance in engineering education through data-driven, transparent, and scalable methods<\/p>\n Developed by Dr. Wan Rosmiza Zana Wan Dagang<\/strong> from the Department of Biosciences<\/strong>, AiEdu integrates cognitive, affective, and psychomotor<\/strong> dimensions of learning to provide a holistic and evidence-based assessment approach. This innovative model was implemented during the Bioprocess Plant Design Competition 2024<\/strong>, where 11 student teams demonstrated their engineering creativity through process flow designs and functional prototypes.<\/p>\n A New Era of Smart Assessment<\/strong><\/p>\n Traditional competition assessments often rely on manual scoring, which can be subjective, inconsistent, and time-consuming. AiEdu addresses these challenges by incorporating machine learning<\/strong> into the evaluation process.<\/p>\n The framework captures and analyzes three dimensions of student performance:<\/p>\n Using these inputs, AiEdu\u2019s Random Forest model<\/strong> achieved an impressive R\u00b2 value of 0.994<\/strong>, accurately predicting judges\u2019 scores with minimal error. This high precision showcases the potential of AI to transform student evaluation into a more objective and data-driven process.<\/p>\n Key Insights from the AiEdu Model<\/strong><\/p>\n Analysis from AiEdu revealed that the \u201cEquipment Specifications\u201d<\/strong> and \u201cCreativity\u201d<\/strong> criteria were the most influential in determining student success, contributing 28.8%<\/strong> and 17.9%<\/strong> respectively to final score predictions.<\/p>\n Interestingly, emotional sentiment<\/strong> and learning gains<\/strong> also played measurable roles, suggesting that both motivation and engagement correlate positively with performance.<\/p>\n \u201cThe AiEdu model demonstrates that emotional and reflective data are just as important as technical proficiency. It helps educators understand how students learn \u2014 not just what they produce,\u201d said Dr. Wan Rosmiza Zana<\/strong>.<\/p>\n Through this integrated approach, AiEdu offers educators a clearer, evidence-based view<\/strong> of how different learning factors \u2014 technical, cognitive, and emotional \u2014 contribute to student achievement.<\/p>\n Transforming Learning Through Data Analytics<\/strong><\/p>\n The success of AiEdu in the Bioprocess Plant Design Competition demonstrates how AI can serve as a pedagogical partner<\/strong> rather than a replacement for human assessment. It enables educators to refine rubric design, calibrate judge evaluations, and provide real-time, constructive feedback to students.<\/p>\n Moreover, the project aligns strongly with the New Academia Learning Innovation (NALI)<\/strong> principles by promoting novelty<\/strong>, creativity<\/strong>, and applicability<\/strong> in teaching and learning practices. AiEdu represents an AI-augmented education system<\/strong> where transparency, fairness, and personalized learning are central to academic growth.<\/p>\n \u201cOur aim is to create an assessment model that is fair, scalable, and data-informed. AiEdu bridges technology and pedagogy to make learning analytics accessible to all educators,\u201d added Dr. Rosmiza.<\/p>\n A Vision for the Future of AI in Education<\/strong><\/p>\n The AiEdu framework not only enhances evaluation accuracy but also opens new avenues for research in learning analytics, sentiment-based feedback, and AI-assisted academic evaluation<\/strong>. Its flexible architecture allows for future adaptation in other STEM courses, competitions, and even professional training programs.<\/p>\n By combining machine learning models<\/strong> with human-centered education, AiEdu embodies UTM\u2019s commitment to AI-driven educational transformation<\/strong>, ensuring that assessment is both scientifically rigorous<\/strong> and student-centered<\/strong>.<\/p>\n \ud83d\udccd Project Title:<\/strong> AiEdu: AI-Driven Predictive Model for Evaluating Educational Outcomes in Bioprocess Plant Design Competition<\/em> \ud83d\udd17 Discover more educational and research innovations at:<\/strong> https:\/\/science.utm.my\/utmfsresearch\/<\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column _builder_version=”4.27.4″ _module_preset=”default” type=”1_2″ theme_builder_area=”post_content”][et_pb_image src=”http:\/\/science.utm.my\/utmfsresearch\/wp-content\/uploads\/sites\/642\/2025\/10\/Wan-Rosmiza-2.jpg” _builder_version=”4.27.4″ _module_preset=”default” theme_builder_area=”post_content” title_text=”Wan Rosmiza 2″ hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_image src=”http:\/\/science.utm.my\/utmfsresearch\/wp-content\/uploads\/sites\/642\/2025\/10\/Wan-Rosmiza-3.png” _builder_version=”4.27.4″ _module_preset=”default” theme_builder_area=”post_content” title_text=”Wan Rosmiza 3″ hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_image src=”http:\/\/science.utm.my\/utmfsresearch\/wp-content\/uploads\/sites\/642\/2025\/10\/Wan-Rosmiza-1.jpg” _builder_version=”4.27.4″ _module_preset=”default” theme_builder_area=”post_content” title_text=”Wan Rosmiza 1.” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" The Faculty of Science, Universiti Teknologi Malaysia (UTM) continues to lead innovation in digital education with the development of AiEdu, an artificial intelligence (AI) framework designed to evaluate student performance in engineering education through data-driven, transparent, and scalable methods Developed by Dr. Wan Rosmiza Zana Wan Dagang from the Department of Biosciences, AiEdu integrates cognitive, […]<\/p>\n","protected":false},"author":390,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[8],"tags":[],"class_list":["post-2620","post","type-post","status-publish","format-standard","hentry","category-innovation"],"_links":{"self":[{"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/posts\/2620","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/users\/390"}],"replies":[{"embeddable":true,"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/comments?post=2620"}],"version-history":[{"count":4,"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/posts\/2620\/revisions"}],"predecessor-version":[{"id":2627,"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/posts\/2620\/revisions\/2627"}],"wp:attachment":[{"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/media?parent=2620"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/categories?post=2620"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/science.utm.my\/utmfsresearch\/wp-json\/wp\/v2\/tags?post=2620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
\ud83d\udc69\u200d\ud83c\udfeb Principal Investigator:<\/strong> Dr. Wan Rosmiza Zana Wan Dagang<\/em>, Faculty of Science, UTM
\ud83d\udce7 Contact:<\/strong> rosmiza@utm.my<\/p>\n