Taklif.AI: LLM-Powered Platform for Interest-Based Personalized College Assignments

TL;DR

Taklif.AI is a novel platform that uses LLMs to generate personalized college assignments based on students' interests, aiming to improve engagement and reduce unethical practices.

Contribution

It introduces a structured prompt engineering pipeline with guardrails to incorporate extracurricular interests and cultural contexts into assignment generation.

Findings

84% of participants rated personalization as beneficial

Preliminary testing shows positive reception among students and educators

System architecture leverages Llama 3.3 70B with serverless AWS deployment

Abstract

Educators face significant challenges in creating engaging, personalized assignments that accommodate students' diverse interests and cognitive abilities. Traditional one-size-fits-all assignments frequently lead to decreased student engagement and increased reliance on unethical practices such as plagiarism. To address these challenges, we present Taklif.AI, a platform that leverages Large Language Models (LLMs) to automatically generate personalized assignments tailored to individual student interests. Unlike existing AI-powered educational platforms that personalize based on academic performance metrics alone, Taklif.AI incorporates students' extracurricular interests and cultural contexts into the assignment generation process through a structured prompt engineering pipeline with input and output guardrails. The platform employs a serverless architecture on AWS with Next.js, using Llama 3.3 70B as the primary LLM via LiteLLM for multi-provider load balancing and LangChain for prompt orchestration. We describe the system architecture, the prompt design methodology, and the guardrails framework that ensures output quality. Preliminary user acceptance testing with 68 participants (65 students and 3 educators) indicates positive reception, with 84% of participants rating the personalization feature as beneficial. We discuss the platform's current capabilities and limitations, and outline directions for rigorous empirical evaluation of learning outcomes.