Development of Multi-Model AI Assistant and User Acceptance Evaluation for Student Academic Tasks

Authors

  • Muhammad Reza Redo Islami✉ Politeknik Negeri Lampung Author
  • Dwirgo Sahlinal Politeknik Negeri Lampung Author
Diajukan 15 Jan 2026 Diterbitkan 15 Mei 2026
DOI: https://doi.org/10.59025/b4ba9209

Keywords:

artificial intelligence, large language model, user acceptance evaluation, TAM, academic assistant

Abstract

This study develops a multi-model artificial intelligence academic assistant utilizing four large language models (LLMs) through the OpenRouter API platform, including Llama 3.3 70B, DeepSeek R1 Distill Llama 70B, Gemma 3 27B, and Phi-4. System development employs the Prototype SDLC methodology with an iterative approach. User acceptance evaluation was conducted using the Technology Acceptance Model (TAM) involving 30 student respondents from Politeknik Negeri Lampung. Results indicate Perceived Usefulness (PU) of 4.32, Perceived Ease of Use (PEOU) of 4.18, and Behavioral Intention to Use (BIU) of 4.27, indicating a high level of acceptance. The system successfully integrates responses from all four models simultaneously and presents structured comparative results, providing students with a more comprehensive perspective for completing academic tasks.

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How to Cite

Development of Multi-Model AI Assistant and User Acceptance Evaluation for Student Academic Tasks. (2026). Journal of Technology and Data Science, 4(1), 299-311. https://doi.org/10.59025/b4ba9209