How to use this guide

This guide is designed to help you effectively teach algorithmic literacy concepts to your students. It offers lesson plans that include learning outcomes, instructional activities, and reflections, which can be integrated into the workshops, used as standalone lessons, or integrated into self-paced tutorials or lectures.

Literacies & Competencies

All lessons are based on the different components of the ACRL Framework for Information Literacy for Higher Education, knowledge practices and dispositions, specifically of the frames “Authority Is Constructed and Contextual,” “Information Creation as Process”, “Information Has Value,” and “Searching as Strategic Exploration.”

In addition, this guide builts on BC’s Digital Literacy Framework, specifically such frames as “ Critical Thinking, Problem Solving, and Decision Making,” “Digital Citizenship,” and “Technology Operations and Concepts.”

Students will have the ability to:

  • Recognize the impact of the algorithmic systems on their everyday life, and on broader social interactions;
  • Understand the inherent biases that technologies and algorithms perpetuate by amplifying social disparities and acknowledge algorithmic bias as a systemic problem;
  • Critically evaluate the impact of algorithmic systems in a variety of situations and contexts;
  • Recognize the importance of privacy when it comes to different digital tools;
  • Apply social justice lens to the algorithm awareness and understand the importance of one’s sense of agency over the algorithmic systems.


This guide is crafted with a diverse audience in mind, catering to students across different educational levels, such as high school, college, and primarily undergraduate university students. Additionally, it can also be adapted to the general public by increasing the level of complexity in reflections and discussion questions. As you explore this guide, always bear in mind the specific context relevant to your learning environment.

Curricular Context & Adaptability

The lessons included in this guide are versatile and adaptable to various educational settings. They are designed to seamlessly integrate into both synchronous and asynchronous digital learning environments, as well as traditional in-person sessions. While many of these lessons are stand-alone and suitable for settings like library workshops, they are equally effective when incorporated into specific courses, lectures, or educational activities. Furthermore, the knowledge and skills imparted in these lessons transcend disciplinary boundaries, making them applicable across a wide range of academic contexts and relevant to a broad audience.


​​It also has the flexibility to include collaborative instructional partners from diverse backgrounds and disciplines, fostering a multidisciplinary approach.

Technology use

The audience will need to be equipped with access to a computer with access to the internet and various software applications. Additionally, they may need to have access to specific resources shared in this guide, such as websites, social media platforms, learning objects, software applications, collaborative software, etc. In some instances, video conferencing tools would need to be used if the lessons are delivered online.


When using activities in this guide, it is essential to consider students’ privacy while engaging with various technologies and tools. Some of the tools (like access to an Amazon account) might not be available to younger students. In those cases, feel free to modify the activity to meet the needs of the specific population group.

Structure of this guide

To make the most of this resource, follow the structure of the lessons outlined below. By following this structure, you can create a well-rounded and engaging learning experience for your students, by offering them essential skills and knowledge to navigate the increasingly complex world of algorithmic systems.


Each section of this guide is labelled with a title that corresponds to the specific topic. Use these titles as reference points to navigate through the content.


Contains a brief overview of the topic.

Learning Outcomes

At the beginning of each section, you’ll find a list of learning outcomes These are intended to clarify what your students should be able to understand and apply after completing a specific activity.

Lecture outline

Each section includes concise lecture materials, which may consist of embedded videos or text. These resources offer an overview of the topic. Feel free to integrate them into your teaching approach, adapting them to suit your classroom’s needs.


This guide includes several activities for each section to reinforce learning and encourage active engagement. These activities consist of experiential exercises to learn the concepts related to algorithmic literacy.

Reflection questions

Each activity is followed by reflection questions that will allow students to reflect on their learning about a specific topic. Reflection questions can be used as opportunities for class discussions, group work, or individual reflection.