IntelliA: an intelligent educational assignment practicing platform driven by generative AI

Purpose Educational assignments are pivotal for student learning, yet educators face the dual burden of creating meaningful questions and providing timely, individualized feedback. This study introduces IntelliA, a generative AI-powered platform designed to address these challenges by holistically automating the entire assignment lifecycle, from pedagogically aware question generation to concept-centric evaluation, thereby alleviating instructor workload and enhancing student learning outcomes. Design/methodology/approach IntelliA’s core innovation is a synergistic architecture that operationalizes pedagogical principles directly into the AI-driven assignment workflow. For question generation, its pedagogical primitive-driven system allows educators to design assessments at a conceptual level – by selecting instructional goals – rather than manually crafting prompts. For evaluation, a novel three-stage, rubric-driven methodology is designed to produce pedagogically valuable feedback, grounding its supportive guidance in objective, evidence-based scoring. These subsystems are unified by a unified quality augmentation module, creating a self-reinforcing loop that continuously refines the system’s pedagogical alignment. This architecture’s ability to enhance both educational quality and operational efficiency was rigorously validated through a semester-long university deployment, assessed via a multi-faceted framework of instructor interviews, student surveys and cross-model benchmarking. Findings The results demonstrate that IntelliA significantly reduces assignment creation time for instructors while maintaining high pedagogical quality in generated questions. Student surveys revealed high satisfaction with the grading accuracy and the personalized, actionable feedback, which they reported as instrumental to their learning. The findings confirm that an architecturally sophisticated AI system can successfully move beyond simple automation to enhance both operational efficiency and educational quality. Research limitations/implications Our study’s validation was conducted within a single university STEM course, which limits the generalizability of our findings. Future research should expand the library of pedagogical primitives to diverse domains (e.g. humanities and K-12) and validate their effectiveness in these new contexts. Additionally, a more rigorous, controlled experiment is needed to quantitatively measure efficiency gains in terms of time-on-task and cognitive load. This work signals a shift in AI-in-education research, moving from single-task automation toward designing synergistic, architecturally aware systems. Practical implications IntelliA offers a practical, scalable solution for educators to significantly reduce the workload associated with assignment creation and feedback personalization. By automating routine tasks, the platform empowers instructors to shift their focus from content generation to higher-value pedagogical activities, such as refining learning objectives and providing nuanced student support. This model demonstrates how AI can be integrated into educational workflows as a collaborative partner, enhancing both operational efficiency and the quality of pedagogical practice. Social implications The widespread adoption of synergistic AI systems like IntelliA could democratize access to high-quality, personalized learning support, potentially narrowing educational equity gaps. However, it also raises critical social questions about the future of the teaching profession and the risk of over-reliance on automation. The key social implication is the need for a public discourse on responsible AI integration in education, ensuring that technology serves to amplify educator agency and enhance human-centric learning, rather than deskilling teachers or creating uniform, passive learning pathways. Originality/value This study presents a significant leap forward by proposing a novel, synergistic system architecture for AI in education. Unlike prior systems that address isolated tasks, IntelliA offers a holistically integrated and self-improving ecosystem. By demonstrating how to embed pedagogical principles directly into an AI’s core logic, IntelliA provides a scalable and flexible blueprint for a new generation of educational tools. This approach moves beyond simple automation, fostering a new dynamic where AI functions not as a mere instrument but as a genuine partner in the pedagogical process.