![\"Yutong](\"https:\/\/fass.nus.edu.sg\/elts\/wp-content\/uploads\/sites\/5\/2026\/07\/talking-to-ai_yutong-liu_kingston-school-of-art_5700x3200.jpg\" "\\"talking-to-ai_yutong-liu_kingston-school-of-art_5700x3200\\"")
\n\t\t\t\t\t\tYutong Liu & Kingston School of Art \/ https:\/\/betterimagesofai.org \/ https:\/\/creativecommons.org\/licenses\/by\/4.0\/\n\t\n\t\t\t\t\t\tYutong Liu & Kingston School of Art \/ https:\/\/betterimagesofai.org \/ https:\/\/creativecommons.org\/licenses\/by\/4.0\/\n\tAs AI systems become embedded in social, cultural, and institutional life, understanding and designing human-AI interactions has become a core competency across sectors. This programme equips students in the humanities and social sciences with the quantitative, computational, and AI skills to navigate these systems, complementing their primary majors with a structured X+AI pathway. Graduates will be prepared for the evolving demands of research, policy, and professional practice across sectors.<\/p>\n
The curriculum develops fluency with computational and AI methods spanning text, data, and geospatial analysis, equipping students to solve disciplinary problems, select appropriate tools, and interpret results critically. Emphasis is placed on combining technical rigour with interpretive depth and ethical practice, preparing students to work responsibly at the intersection of human inquiry and AI capability by evaluating outputs for limitations, uncertainty, and appropriate use in context. Students gain hands-on experience working with diverse data sources, learning to extract meaningful insights while maintaining scholarly integrity and ethical standards. Graduates will emerge prepared for diverse career paths in academia, public service, think tanks, cultural institutions, media organizations, and private sector roles requiring analytical thinking, data literacy and AI competences. The programme provides opportunities for students to orchestrate AI-enabled workflows for multi-step disciplinary questions. By combining technical expertise with domain insight and governance awareness, students develop unique interdisciplinary capabilities to address complex, real-world challenges in an increasingly data- and AI-rich environment.<\/p>\n
Students must pass a minimum of five courses (20 units) from the list of the Minor, including<\/p>\n(1) one compulsory core course; NM2228 Data Intelligence for Social Applications<\/p>\n (2) AI Stewardship and Ethics – select 1 (4 Units)<\/u><\/strong><\/p>\nGI3101 GeoAI for Good (3) Data-, AI-, and Discipline-centric Electives – select 2 (8 Units)<\/u><\/strong><\/p>\nBT1101% Introduction to Business Analytics (4) Capstone – UROP (4 Units)<\/u><\/strong><\/p>\n Within the UROP format, students will undertake a supervised research project that draws on the methods, ethics, and domain knowledge developed across the programme, translating analytical competence into a substantial piece of applied or scholarly work. Students should demonstrate that they can orchestrate data analytical tools and AI insights to solve complex real-world problems and drive data-informed decision-making in their field. The UROP topic and scope should be approved by the Minor convenor.<\/p>\n\n","protected":false},"excerpt":{"rendered":" Minor in Human-AI Systems Yutong Liu & Kingston School of Art \/ https:\/\/betterimagesofai.org \/ https:\/\/creativecommons.org\/licenses\/by\/4.0\/ As AI systems become embedded in social, cultural, and institutional life, understanding and designing human-AI interactions has become a core competency across sectors. This programme equips students in the humanities and social sciences with the quantitative, computational, and AI skills to navigate these systems, complementing their primary majors with a structured X+AI pathway. Graduates will be prepared for the evolving demands of research, policy, and professional practice across sectors. The curriculum develops fluency with computational and AI methods spanning text, data, and geospatial analysis, equipping students to solve disciplinary problems, select appropriate tools, and interpret results critically. Emphasis is placed on combining technical rigour with interpretive depth and ethical practice, preparing students to work responsibly at the intersection of human inquiry and AI capability by evaluating outputs for limitations, uncertainty, and appropriate use in context. Students gain hands-on experience working with diverse data sources, learning to extract meaningful insights while maintaining scholarly integrity and ethical standards. Graduates will emerge prepared for diverse career paths in academia, public service, think tanks, cultural institutions, media organizations, and private sector roles requiring analytical thinking, data literacy and AI […]<\/p>\n","protected":false},"author":354,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"full-width-container","site-content-style":"unboxed","site-sidebar-style":"unboxed","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-33991","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/pages\/33991","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/users\/354"}],"replies":[{"embeddable":true,"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/comments?post=33991"}],"version-history":[{"count":3,"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/pages\/33991\/revisions"}],"predecessor-version":[{"id":34000,"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/pages\/33991\/revisions\/34000"}],"wp:attachment":[{"href":"https:\/\/fass.nus.edu.sg\/elts\/wp-json\/wp\/v2\/media?parent=33991"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n(2) one AI Stewardship & Ethics course;
\n(3) two Data-, AI-, and Discipline-centric Electives; and
\n(4) one UROP serving as a practical capstone, with topics and scope approved by the Minor convenors.\n
\n(1) Core Course (Compulsory, 4 Units)<\/strong><\/u>\n
\nNM3254 Artificial Intelligence, Algorithms and Governance
\nPH1201 AI, Algorithms, and Ethics
\nPH3401 AI Safety and Alignment
\nSC3210 AI and Society\n
\nEC2314 Discovering Economics through Data
\nEC4308 Machine Learning and Economic Forecasting
\nEC4501 Economics and Computation
\nEL2216 Humans vs. Language Models: Linguistic Behaviours
\nEN2215 Literature and Culture in the Age of AI
\nGE2234 Geospatial Data Collection and Digital Mapping
\nGI4101 GeoAI in Urban Applications
\nGI4102 Computational Simulation for Social Science
\nHS2931 Smart City \/ Intelligent Humans
\nNM3253 Fandoms to Algorithms: K-Pop, Data and Culture
\nPH3245 Language and Thought
\nPL4245 Data Science for Psychology: Methods and Applications
\nPS4344 Artificial Intelligence and Public Policy
\nSW3224 CTRL+SHIFT+FUTURE: Social Work and Technology
\nSW4233 Digital Technologies in Children and Youth Services
\nTS2244 Theatre, Big Data and Artificial Intelligence\n