D0142 - AUGMENTED INTELLIGENCE FOR ECONOMISTS: PROMPT ENGINEERING AND PRACTICAL APPLICATIONS

Learning outcomes

"• Critically evaluate and use artificial intelligence tools for academic and professional tasks in Economics, understanding their capabilities, limitations, and implications, and maintaining human judgment and responsibility in the decision-making process. • Apply prompt engineering techniques to support learning and problem-solving activities in Economics, including exam preparation, comprehension checks, and practice exercises, without delegating core analytical reasoning to AI systems. • Identify, assess, and synthesize reliable academic sources to produce a structured and coherent literature review on an economic topic of interest, using AI tools to enhance efficiency while preserving critical evaluation. • Collect, analyze, and communicate insights from textual media data, using AI-assisted methods to examine how the coverage of an economic topic evolves over time and to present results through appropriate tables and graphical representations. • Design and deliver clear, concise, and persuasive oral communications, including timed presentations and structured debates, using AI as a support tool for preparation while demonstrating independent reasoning and argumentation."

Course contents

The course is built around the concept of augmented intelligence: students will learn how to use artificial intelligence tools in a way that complements and enhances, rather than replaces, human judgment, analytical reasoning, and communication skills.

The course is organized into five three-hour in-person classes. Each class combines a short lecture with hands-on activities in which students apply AI tools to tasks that are central to the training of economists.

1. AI tools for learning and exam preparation

The first class focuses on the use of AI tools to support individual study. Topics include the organization and synthesis of lecture notes, the generation of comprehension questions, the creation of new practice exercises similar to exam questions, automated feedback, and preparation for oral exams. Particular attention will be paid to the distinction between using AI to support learning and using AI to bypass understanding.

2. Academic writing and literature reviews

The second class is devoted to academic writing and research. Students will learn how to identify reliable academic sources, assess their relevance and credibility, and use AI tools to support the production of a short structured literature review on an economic topic of their choice. The activity will require students to select and critically assess at least ten reliable academic sources.

3. Textual data, media analysis, and economic communication

The third class introduces students to the collection and analysis of textual media data. Students will learn how to collect news articles over a given time horizon using predefined keywords, organize and analyze the resulting corpus, and produce a short data-driven article for a non-specialist audience. The article will discuss how media coverage of a given economic or social topic has evolved over time and will be supported by either two tables, two graphs, or one table and one graph.

4. AI-assisted oral communication

The fourth class focuses on oral communication skills. Students will use AI tools to support the design, revision, and rehearsal of short oral presentations on topics related to economics, finance, or public policy. Particular attention will be paid to structure, timing, clarity, and the ability to communicate complex ideas concisely. Students will prepare and deliver a strictly timed five-minute presentation, individually or in small groups depending on class size.

5. Debate, persuasion, and critical reasoning

The fifth class is organized around a structured debate activity. Students will be assigned opposing positions on a given statement and will be asked to construct consistent arguments under tight time constraints. The activity is designed to develop students’ ability to distinguish factual accuracy from persuasive effectiveness, critically evaluate arguments, and communicate under pressure. Depending on class size, the debate contest may be organized in teams, with students also involved as evaluators and discussants.

Readings/Bibliography

Required material

There is no required textbook for this course. Required material will consist of lecture slides, short readings, practical instructions, datasets, prompts, and examples made available through the course’s e-learning platform, Virtuale.

These materials are necessary to complete the course activities and to obtain the pass/fail assessment.

Additional material

Further optional material may be suggested for students who wish to deepen specific topics, such as large language models, responsible AI, research workflows, text analysis, or science communication.

Teaching methods

Teaching will combine short lectures, hands-on exercises, group work, peer discussion, and in-class presentations or debates.

Each class will introduce a specific set of AI-assisted tasks and will then guide students through their practical application. Students will be asked to use AI tools directly, compare alternative prompts, evaluate the quality of AI-generated outputs, revise those outputs critically, and document the choices they make.

The course is strongly practice-oriented. Active participation is therefore highly recommended. Students are expected to bring a laptop or tablet to class and to use AI tools, online resources, and Virtuale during the activities.

Assessment methods

The course is assessed on a pass/fail basis.

Assessment is based on the completion of the 5 practical activities carried out during the course, which will be then submitted through Virtuale. The portfolio is designed to verify that students have achieved the learning outcomes of the course and are able to use AI tools critically, responsibly, and effectively.

The portfolio will include the following components:

• an AI-assisted exam-preparation tool;
• a short structured literature review on an economic topic, based on at least ten reliable academic sources;
• a short data-driven article based on textual media data, supported by either two tables, two graphs, or one table and one graph;
• a video-recorded 5-minute presentation, prepared with the support of AI tools;
• a report on the preparation and outcome of the debate activity, focusing on argument quality, persuasion, factual accuracy, and the role of AI.

To pass the course, students must complete all mandatory portfolio components and show that they are able to use AI tools as support for their own reasoning, rather than as a substitute for it.

Students who do not submit the required portfolio components, or whose submissions show substantial delegation of the task to AI without critical assessment, will not pass the course.

Students with learning disorders and/or temporary or permanent disabilities are invited to contact the responsible University office as soon as possible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ). The office will propose possible adjustments. Requests for adaptation must be submitted to the lecturer at least 15 days before the relevant assessment deadline; the lecturer will assess the appropriateness of the proposed adjustments, taking into account the teaching objectives of the course.

Use of generative AI in assessment activities

Since the course is specifically devoted to the responsible use of AI tools, the substantial use of generative AI is allowed and expected in the assessment activities.

However, students must use AI tools critically and transparently. For each portfolio component, students must document how AI was used, what prompts or strategies were adopted, how the output was evaluated, and what changes were made by the student.

AI-generated content must not be submitted without critical revision. Students remain fully responsible for the accuracy, reliability, originality, and integrity of the work they submit. Fabricated references, unsupported claims, unverified data, or misleading use of AI-generated material will be considered incompatible with successful completion of the course.

Teaching tools

The course will use Virtuale for lecture slides, instructions, datasets, prompts, examples, submissions, and additional material.

Students are expected to have access to a laptop or tablet and to an internet connection during the classes. They will also need access to at least one generative AI tool. When possible, the course will prioritize tools that are freely accessible or available through institutional licenses.

The teaching tools used in the course include:

• generative AI tools for text generation, revision, summarization, and feedback;
• academic search engines and bibliographic databases;
• tools for organizing and analyzing textual data;
• spreadsheet or basic data-visualization tools;
• Virtuale for course material, instructions, and submissions.

Office hours

See the website of Maria Bigoni