INFO 287-15 (2-Units)
Seminar in Information Science
AI In The Library
Spring 2020 Syllabus

Andromeda Yelton
Office Location: Online
Office Hours: By appointment

Syllabus Links
Canvas Login and Tutorials
iSchool eBookstore

Canvas Information: Courses will be available beginning January 23, 2020, at 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.

This 2-unit course runs from March 9th - May 11th

You will be enrolled in the Canvas site automatically.

Course Description

This course covers what artificial intelligence is and critical analysis of AI systems, in the context of real-world library and archival AI applications. Programming skills not required.

Course Requirements


The general plan of assignments is given below. Specifics will be available in Canvas.

Course Calendar

All assignments are due at midnight California time on the last day of their week, except for the final project. The final project is due on Wednesday, May 6, at midnight California time, in order to give time for Q&A.

Week Dates Topic Assignments due
1 March 9 - 15 Machine Learning Techniques:
Introduction to ML
Mental model writeup #1
(CLO 1)
2 March 16 - 22 Machine Learning Techniques:
The NIU Dime Novels Collection and text mining
3 March 23 - 29 Data and its Discontents:
The Charles Teenie Harris Archive and messy data
Final project proposal
(CLO 1, 2, and/or 3)
4 March 30 - April 5 Spring break! Have fun
Take a break
Engage in self-care
5 April 6 - 12 Data and its Discontents:
Algorithmic bias and what machines learn
Dataset investigation
(CLO 3)
6 April 13 - 19 The Search for Meaning:
HAMLET and neural nets
7 April 20 - 26 The Search for Meaning:
Interpretability and intent
Mental model writeup #2
(CLO 1)
8 April 27 - May 3 AI and You:
The URI AI lab: AI as service model
(CLO 2)
9 May 4 - 10 AI and You:
Final projects
Final project
(CLO 1, 2, and/or 3)


Assignment Percentage Due date
Participation 25 Ongoing
Mental model writeup #1 5 March 15
Final project proposal 5 March 29
Data set investigation 15 April 12
Mental model writeup #2 15 April 26
Memo 15 May 3
Final project 20 May 6
Please note this is a Wednesday, unlike the other assignments.

Other Relevant Information:

Discussion is an important element of this course, as it is where we will explore many of the issues raised in the reading. I expect participants to not merely be curious and thoughtful about the material, but also to hold space for their classmates to learn, ask questions, disagree, and admit uncertainty. To that end, the course participation rubric (available in Canvas) will include social rules based on ALA's Statement of Appropriate Conduct and the Recurse Center's Social Rules.

Course Workload Expectations

Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.

Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.

Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.

Course Prerequisites

INFO 200, other prerequisites may be added depending on content

Course Learning Outcomes

Upon successful completion of the course, students will be able to:

  1. Understand and explain the basics of AI: both its underlying principles and common machine learning techniques.
  2. Discuss realistic ways that AI can be a part of library services.
  3. Critically analyze potential pitfalls of AI systems, including the role of their underlying data sets and their ramifications in society.

Core Competencies (Program Learning Outcomes)

INFO 287 supports the following core competencies:

  1. H Demonstrate proficiency in identifying, using, and evaluating current and emerging information and communication technologies.


No Textbooks For This Course.

Grading Scale

The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:

97 to 100 A
94 to 96 A minus
91 to 93 B plus
88 to 90 B
85 to 87 B minus
82 to 84 C plus
79 to 81 C
76 to 78 C minus
73 to 75 D plus
70 to 72 D
67 to 69 D minus
Below 67 F


In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:

  • C represents Adequate work; a grade of "C" counts for credit for the course;
  • B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
    For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
  • A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.

Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).

University Policies

Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: Make sure to visit this page, review and be familiar with these university policies and resources.

In order to request an accommodation in a class please contact the Accessible Education Center and register via the MyAEC portal.

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