About
About AlgoGPT
Fostering Learner Agency and Self-Regulated Learning through Adaptive AI Scaffolding in Programming Education
Overview
AlgoGPT is an AI-powered learning platform conceptualised and led by Dr Owen Noel Newton Fernando, Senior Lecturer at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore.
Developed with support from an NTU EdeX grant, AlgoGPT addresses a key challenge in computing education: providing personalised learning support while ensuring students continue to develop independent thinking, programming skills and problem-solving abilities.
Grounded in Self-Regulated Learning, Cognitive Load Theory and Productive Struggle, AlgoGPT encourages students to learn with AI rather than simply depend on AI-generated answers. The platform is integrated into SC1007 Data Structures and Algorithms through CCDS's Learn with AI initiative, supporting nearly 1,000 students.
AlgoGPT offers over 400 programming practice questions and over 300 multiple-choice questions (MCQs) covering Data Structures and Algorithms. Students can practise at their own pace, assess their understanding and receive personalised learning support based on their progress.
Our Educational Approach
AlgoGPT draws on three complementary educational principles to support meaningful learning with AI.
Self-Regulated Learning · Taking Ownership of Learning
AlgoGPT encourages students to take an active role in managing their learning through four processes: planning, monitoring, control and reflection. Students identify areas for improvement, monitor their understanding, adjust their problem-solving strategies and reflect on feedback. Personalised learner profiles, adaptive practice recommendations and learning analytics support these processes.
Cognitive Load Theory · Managing Learning Complexity
Programming requires students to understand problems, select appropriate algorithms and translate their reasoning into code. AlgoGPT provides structured hints, guiding questions and adaptive scaffolding to help students manage the cognitive demands of complex programming tasks. By providing support when needed rather than immediately presenting complete solutions, the platform aims to reduce unnecessary cognitive load while preserving the mental effort required for learning.
Productive Struggle · Learning Through Challenges and Mistakes
AlgoGPT encourages students to work through meaningful challenges instead of immediately relying on AI-generated answers. Students are guided to investigate difficulties, test their reasoning and revise their solutions. In Navigator Mode, deliberately introduced errors in AI-generated code create opportunities for students to identify mistakes and explain how to correct them.The aim is to make struggle productive through appropriate guidance and feedback, rather than leaving students frustrated or dependent on AI.
Four Learning Modes
AlgoGPT supports four complementary learning modes that balance independent problem-solving with structured AI assistance.
- 1
No-AI Mode
Independent Problem-Solving
Students solve programming problems independently without AI assistance, applying their knowledge and developing confidence in their own problem-solving abilities. This mode provides opportunities to practise and assess independent understanding.
- 2
Normal Mode
Personalised AI Guidance
Students receive personalised hints, explanations and guiding questions to help them understand programming concepts and overcome difficulties without receiving complete code solutions. The AI provides assistance while encouraging students to develop their own answers.
- 3
Driver Mode
Student-Led Programming
Students write their own code while AI provides Socratic questions and structured guidance. This mode supports productive struggle by encouraging students to reason through problems, make decisions and refine their solutions rather than relying on AI to write code for them.
- 4
Navigator Mode
Critical Evaluation of AI-Generated Code
Students explain their intended approach and evaluate AI-generated code containing deliberately introduced errors. They identify mistakes, assess the proposed solution and decide whether to accept or reject it. This mode encourages students to question AI outputs, investigate errors and develop independent reasoning and code evaluation skills.
Together, these modes provide different levels of AI assistance while keeping students actively involved in the learning process. Driver and Navigator Modes particularly emphasise learner agency, productive struggle and critical engagement with AI-generated solutions.
Personalised and Adaptive Learning
Beyond its four learning modes, AlgoGPT provides personalised learning support through several integrated features:
Programming Practice
Over 400 programming questions covering fundamental and advanced topics in Data Structures and Algorithms.
Conceptual Assessment
Over 300 MCQs to assess understanding, reinforce key concepts and identify knowledge gaps.
Adaptive Recommendations
Personalised practice recommendations based on MCQ responses, laboratory submissions and programming attempts.
Learner Profiles
Individual profiles that help students monitor their progress, understand their proficiency and identify areas requiring improvement.
Learning Analytics
Dashboards that provide students and educators with insights into learning progress, common difficulties and areas requiring additional support.
These features support students in planning their learning, monitoring their understanding, adjusting their strategies and reflecting on their progress. Educators can also use learning analytics to identify common misconceptions and provide more targeted support.
Awards and Recognition
2026 Global MOOC and Online Education Alliance (GMA) Award · Outstanding Courses
SC1007 Data Structures and Algorithms, incorporating AlgoGPT, was selected as one of ten winners of the 2026 GMA Award for Outstanding Courses.
The course achieved the highest overall evaluation score among the ten award-winning courses, according to communication from the GMA organisers. This recognition highlights its innovative approach to personalised learning, independent problem-solving and critical evaluation of AI-generated code.
Official Award Announcement2026 John Cheung Emergent Technology Award · Nanyang Technological University
Dr Owen Noel Newton Fernando was selected for NTU's 2026 John Cheung Emergent Technology Award, recognising his contributions to technology-enhanced education, including the innovative application of AlgoGPT in Data Structures and Algorithms.
Our Team
Principal Investigator and Project Lead
Dr Owen Noel Newton Fernando
Senior Lecturer, College of Computing and Data Science
Faculty Collaborators
Dr Siyuan Liu
Lecturer
College of Computing and Data Science
Nanyang Technological University
Mr. Ong Chin Ann
Lecturer
College of Computing and Data Science
Nanyang Technological University
Current Student Researchers and Developers
Final Year Project (FYP) Students
- Eva Faith Wong Jie Ting
- Ng Qi Cheng
- Jolie Tan (Jolie Chen En)
- Agarwala Grisha
Undergraduate Research Experience on Campus (URECA) Students
- Balakrishnan Vaisiya
- Kway Ler Koon
- Mishra Suhani
- Manoj Siddhanth
- Nikita Saravanan
Current students contribute to the ongoing research, development, testing, evaluation and improvement of AlgoGPT. Their involvement brings valuable student perspectives into the design of AI-supported learning experiences.
Former Student Contributors
- Harvande Advika Sandeep
- Summit Bajaj
We acknowledge the contributions of former students who supported the development and advancement of AlgoGPT.
Research and Publications
AlgoGPT also serves as a research platform for investigating how students learn through interactions with generative AI, particularly when AI-generated solutions contain mistakes that encourage critical evaluation, reflection and independent problem-solving.
Conference Publication
Beyond the Perfect Assistant: Provoking Learning with Flawed AI Partners
Balakrishnan, V., Kway, L. K., Sandeep, A. H., Wong, E. F. J. T., Ong, C. A., & Fernando, O. N. N. (2026). Beyond the Perfect Assistant: Provoking Learning with Flawed AI Partners. In Companion Publication of the 2026 ACM Designing Interactive Systems Conference (DIS Companion '26) (pp. 270–274). Association for Computing Machinery.
This paper explores how deliberately imperfect AI partners can encourage students to question AI-generated solutions, identify errors and engage more actively in programming problem-solving. The approach is reflected in AlgoGPT's Navigator Mode, where students critically evaluate and improve AI-generated code rather than simply accepting it.
doi.org/10.1145/3802974.3809428Featured Article · NTU InsPIRE (2026)
AlgoGPT: Designing an AI Coding Mentor to Foster Productive Struggle and Learner Agency
Published by NTU's Institute for Pedagogical Innovation, Research and Excellence (InsPIRE) on 28 October 2026, this article features AlgoGPT's approach to AI-supported programming education.
It highlights how the platform encourages productive struggle, learner agency and independent problem-solving through structured AI guidance rather than simply providing complete solutions. The article also illustrates how educational principles can inform the design of AI learning experiences that keep students actively engaged in their own learning.
Read the ArticleOur Vision
We envision a future where AI supports students in becoming more capable, confident and independent learners. Our goal is to create learning experiences that combine personalised AI guidance with meaningful opportunities for productive struggle, reflection and critical thinking.
Rather than measuring success solely by what students can accomplish with AI, we focus on what they can understand, explain and solve independently after learning with it.