Business, Economics and Law Behavioural Economics Research Proposal

The research proposal is an authentic task which encourages the application of behavioural economics theory and research methodologies to a real-world scenario. It also encourages the development of skills to research and communicate effectively.

Tutorials provide an opportunity for the course coordinator and tutors to provide students with feedback as the research proposal is being developed (assessment for learning). This enables the course coordinator and tutors to have visibility of the work as it is being developed by students. Part of this includes the students ‘pitching’ their proposal in tutorials for peer and tutor feedback. This is an advantage when marking the final submission in that:

• markers have already seen the development of the proposal and the learning process.
• the proposal is easier to read (and mark) as it has been refined by students during the development and feedback process in tutorials. (See Tips for Implementation below).

As this is an undergraduate course, some students find it challenging to design a research question, write a literature search, and design an appropriate methodology. Hence the teaching approach needs to be designed so that students are provided with ongoing feedback during the development of the research proposal.

It is important to unpack the task and provide scaffolding for the research proposal. Tutorials, in the four weeks leading to the final submission date, are dedicated to teaching students how to:

• use tools for deriving a unique research question (e.g. students are shown how Google Scholar can be used in the literature search to determine the question for their proposal).
• write a literature review .
• communicate effectively using academic writing.
• develop an appropriate method for the research proposal.

During these four weeks, tutors actively facilitate the development of the proposals. This includes providing initial feedback to students on their proposed research question and then by including scaffolding for students throughout the development of the task.

Students also present in tutorials a 90 second pitch during tutorials where they receive feedback on their proposal from peers and tutors.

Also, throughout the course students are given opportunities to learn using GenAI tools. Students in tutorials explore how GenAI tools produce output on behavioural economics, the inherent biases and effective prompt engineering. They also learn how GenAI can best be leveraged as a research tool and specifically how it can help them with their assessment. This includes how it can be used to synthesise literature, as a sounding board for research ideas, and help propose and refine research methodologies.

1. Describe heuristics and biases used in decision making and how these may lead either to suboptimal choices or to effective decision-making under cognitive constraints.
2. Apply the concepts and tools of behavioural economics to a range of real-world situations.
3. Generate better informed appraisals of economic policy discussions than you would have done if you had only applied classical economic theories.

The visibility of the process for developing the proposal in the 4 weeks leading to the due date ensures that tutors (markers) have a good understanding of the developmental learning students have undertaken before the submission of the final research proposal.

The deliberate focus in the course on learning about GenAI promotes the ethical use of large language models (LLMs) in assessment tasks. Students are encouraged to use GenAI (e.g. to learn to write for the genre appropriate for a research proposal). However, the unique question students develop limits the assistance that can provided by GenAI (as LLMs are not trained on the question). Further, as an assessment requirement, the proposal must reference, in a substantial way, at least one academic journal article that has been published since 2023 (for this task in S1 2024). This also mitigates the support that GenAI can provide (as LLMs are unlikely to be trained on the article).

Further, the course coordinator and tutors explain to students that the benchmark for certain criteria has been raised as the use of GenAI is allowed.