Student Consent Form

Approval: 2025/165  |  Research: A Framework for an Effective Integration of Large Language Models (LLMs) in Teaching and Learning Computer Programming in IT Higher Education

Use your official email from Murdoch University Australia, UniKL Malaysia, Taylor’s University Malaysia, or Universiti Utara Malaysia (UUM).
Approval: 2025/165
RESEARCH: A Framework for an Effective Integration of Large Language Models (LLMs) in Teaching and Learning Computer Programming in IT Higher Education
  1. I agree voluntarily to take part in this research. I understand that participation is voluntary and that choosing not to participate will not affect students’ grades, academic standing, or relationships with staff.
  2. I have read the Information Sheet provided and have been given a full explanation of the purpose of this research, the procedures involved and what is expected of me.
  3. I understand that I will:

    a) Consent
    Receive a link to a dedicated, secure Learning Management System (LMS) site created for this research. The site provides a clear description of the study (objectives, requirements, and procedures). My consent will be indicated by ticking a consent checkbox.

    b) Be Given Anonymous Access
    Be provided with randomized login credentials (username and password). On login, the system assigns a random code as my username (e.g., LLM2026_XXXXXXXX). No names, student IDs, or other personally identifiable details are used.

    c) Complete Computer Programming Tasks (20-30 minutes)
    Complete three tasks in one of the following languages—Python, C, or C++—at ascending difficulty levels:
    a) Beginner (~10 minutes)
    b) Intermediate (~10 minutes)
    c) Advanced (~10 minutes)

    d) Use of the LLM Chatbot (throughout tasks)
    A built-in LLM chatbot is available within the LMS for guidance and explanations at any time during the tasks.

    e) Answer Exit Questionnaire (20–30 minutes)
    (i) Demographic Questions (15 Questions): age range, gender, study level, programming experience, and prior use of AI/LLM tools. No identifying personal details are collected.
    (ii) Likert-Scale Items (50 Questions): e.g. assessing perceived usefulness, learning support, and views on LLMs in computer programming education.
    1 – Strongly Disagree
    2 – Disagree
    3 – Neutral
    4 – Agree
    5 – Strongly Agree

    (iii) Open-Ended Questions (5 Questions): optional free-text reflections (sentences, short paragraphs, or bullet points).

  4. I confirm that the researcher has answered all my questions and has explained any potential risks or issues that may arise from my participation.
  5. I confirm that I am sufficiently fluent in English to understand the information provided and to follow the study instructions.
  6. I understand that I may withdraw from the study at any time, without penalty and without providing a reason.
  7. I understand that I will not be identifiable in any publication arising from this research. Participation will be fully anonymous through the use of randomly generated login credentials. Teaching staff will not know which students chose to participate, and no identifiable data will be collected.
  8. I understand that any record of my name or identity (if collected for administrative purposes) will be stored separately from the research data and accessible only to the investigators. All data I provide will be analysed using coded identifiers to ensure anonymity.
  9. I understand that all data will be de-identified prior to analysis and reporting, and findings will be presented to preserve complete anonymity. By ensuring that recruitment, participation, and data handling occur outside the direct student–teacher relationship, risks associated with unequal power dynamics are minimised.
  10. I understand that all information I provide will be treated as confidential and will not be disclosed to third parties, except where disclosure is required by law.

Student Declaration

  • I confirm that the researcher has answered all my questions and has explained any potential risks or issues that may arise from my participation.
  • I confirm that I am sufficiently fluent in English to understand the information provided and to follow the study instructions.
  • I understand that I may withdraw from the study at any time, without penalty and without providing a reason.
  • I understand that I will not be identifiable in any publication arising from this research. Participation will be fully anonymous through the use of randomly generated login credentials. Teaching staff will not know which students chose to participate, and no identifiable data will be collected.
  • I understand that any record of my name or identity (if collected for administrative purposes) will be stored separately from the research data and accessible only to the investigators. All data I provide will be analysed using coded identifiers to ensure anonymity.
  • I understand that all data will be de-identified prior to analysis and reporting, and findings will be presented to preserve complete anonymity. By ensuring that recruitment, participation, and data handling occur outside the direct student–teacher relationship, risks associated with unequal power dynamics are minimised.
  • I understand that all information I provide will be treated as confidential and will not be disclosed to third parties, except where disclosure is required by law.
Typed text signature only. This will appear in the PDF copy.
Signature of Researcher
Marius Maharon
Date: 02/06/2026