Summary	This submission includes material developed to assist CS instructors in covering the three core hours on AI Applications and Societal Impact recently added to the 2023 Computer Science Curricula recommendations. The materials include two sets of slides addressing key issues, such as defining fair and equitable AI applications, dataset bias, algorithmic and evaluation bias, environmental considerations, and a high-level overview of large language models. Additionally, two worksheets are provided to facilitate and assess students' understanding, while promoting reflection on these critical subjects. We estimate that three hours will be needed to cover the lecture material, and the students will need six to nine hours to complete the worksheets independently.
Topics	AI applications, fairness, equity, transparency, sustainability, introduction to Large Language Models and their applications.
Audience	Undergraduate core courses.
Difficulty	Introductory level - the worksheets do not require any prior AI knowledge beyond what is included in the lectures.
Strengths	The worksheets are designed to be unplugged, making them accessible to a wide range of students. Additionally, the instructor is given the flexibility to choose how the worksheets need to be completed (in or out of class, in groups or individually). The slide decks provided are created to assist instructors in delivering the lectures, offering detailed notes to support both preparation and presentation, regardless of their AI expertise.
Weaknesses	The assignment includes some open ended questions, which sometimes can be answered in different ways and require more time for grading. Rubrics are provided to help with this task. Despite the inclusion of the slide decks, instructors will need to spend some time to prepare their presentations and familiarize with the material, depending on their level of familiarity with the topics. And finally, because the development of the AI/ML technologies described in this material moves at a fast pace, adopters may need to update the material with recent developments.
Dependencies	None - the lectures and worksheets are unplugged and have no prerequisites, although the students will depend on the clear delivery of the lectures to be able to complete the exercises.
Variants	The presentations can be shortened if students are already familiar with some topics (e.g. Learning Algorithms - Fundamentals). In this scenario, more time could be spent in time on the worksheets and self-reflection questions.

• Introduction to AI, fairness and bias - slides (.pptx)(.pdf)
• Introduction to AI, fairness and bias - worksheet (.docx)(.pdf)
• Introduction to Large Language Models - slides (.pptx)(.pdf)
• Introduction to Large Language Models - worksheet (.docx)(.pdf)