GPT-Based Student Assistant


Professional Guide to Finding the Perfect Educational Path

Anti-Hallucination Mechanism

Anti-Hallucination Mechanism

Proactive Engagement

Proactive Engagement

Automated Rationale Provision

Automated Rationale Provision

The problem

As higher education becomes more competitive and diversified, both students and institutions are looking for ways to match the right courses and colleges with individual aspirations and academic profiles. 

Students face challenges navigating options due to a lack of accurate, readily accessible information. Slow response times from institutions complicate this further, ultimately preventing students from making informed decisions about their educational future.

Key Needs:

  • Clear and detailed information: Students want to know everything from curriculum and course specifics to faculty qualifications. After all, these factors will shape their educational journey and future careers.
  • Responsive communication: In the digital age, nobody likes to be kept waiting, least of all students seeking answers. Institutions need to aim for speedy and efficient responses to any queries.
  • Personalization: Education is not one-size-fits-all. Each student has unique needs and goals. Tailoring communication and recommendations to individual requirements and aspirations can greatly enhance the student experience.

Addressing these key needs is essential for offering an exceptional customer experience in the education sector. Failure to do so risks alienating prospective students and could result in missed opportunities for both parties.

The Solution

We have developed an AI-based student assistant designed to act as a 24/7 personal academic advisor, providing personalized, accurate information to help students make informed educational choices.

This assistant goes beyond merely answering questions; it actively inquires to gain a comprehensive understanding of each student’s academic needs and aspirations.

Using this nuanced understanding, the assistant then meticulously scans our extensive internal database, which includes data on courses, entry requirements, and faculty qualifications, to generate highly personalized educational recommendations.

Powered by the GPT-3.5 Turbo engine, our system achieves an ideal synergy of robust performance and cost-efficiency. This makes it an indispensable tool for academic institutions and EdTech companies aiming to provide exceptional, personalized educational guidance.

The Results

  • Rich Information Source: Our system delves into a curated knowledge base to provide comprehensive details on courses, entry prerequisites, and faculty qualifications, thus fulfilling the need for clear and detailed information.
  • Fluid Interaction: Modeled after ChatGPT, our solution facilitates fluid, human-like dialogue, addressing the need for timely and responsive communication.
  • Anti-Hallucination Mechanism: In instances of unknown queries, the agent transparently communicates its limitations, ensuring reliable and trustworthy guidance
  • Tailored Content Delivery: The AI agent provides personalized course and university recommendations based on individual skills and aspirations, effectively personalizing the educational journey for each student.
  • Proactive Engagement: Our agent initiates conversations to guide the user toward the most fitting educational options, proactively assisting in their decision-making process.
  • Automated Rationale Provision: The system doesn’t just make suggestions; it explains its recommendations, offering students a deeper understanding of their options.
  • Anti-Hallucination Mechanism: In instances of unknown queries, the agent transparently communicates its limitations, ensuring reliable and trustworthy guidance.

Technologies used

gpt model

GPT 3.5 Turbo

Python logo


Google Cloud Platform




Weaviate Vector Database


The path to success

Step 1: Data Collection

We started by immersing ourselves in data, gathering comprehensive details about universities, courses, and faculties to cover the gamut of potential student queries.

Step 2: Data Cleaning

Following collection, we meticulously cleaned the data, eliminating any inaccuracies or redundancies to ensure the AI model would interact with only the most relevant and precise information.

Step 3: Data Preprocessing

We then organized the cleaned data into a machine-readable format, facilitating its easy manipulation and analysis for integration into machine learning models.

Step 4: Model Selection

Choosing the right AI model was crucial; we selected OpenAI’s GPT-3.5 Turbo for its robust performance capabilities that met our specific needs.

Step 5: Data Embedding

At this juncture, we assigned unique numerical identifiers to each data item, like university names and courses. This embedding process is vital for enabling the AI model to search and identify relevant data quickly.

Step 6: Contextual Prompts

We programmed ‘invisible’ prompts into our chatbot, allowing it to not only answer questions but also initiate conversations and steer dialogue towards helping the user find their ideal educational fit.

Step 7: Semantic Search

This is where data embedding came into play once more, enabling the AI model to conduct contextual searches based on user inputs and provide appropriate course suggestions.

Step 8: Model Testing and Refining

We rigorously tested the model to identify and fix any performance issues, ensuring it met our standards for accuracy and reliability.

9: User Interface Creation

Once we were satisfied with the model’s performance, we designed an intuitive, user-friendly interface to allow for seamless interactions between the AI system and the students.

Step 10: Live Environment Integration

The final step involved integrating our AI-based student assistant into a live operational environment, making it fully accessible to students for their educational planning needs.

See it in action

See it in action


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