Towards More Gender Diversity in CS through an Artificial
Intelligence Summer Program for High School Girls⇤

Marie E. Vachovsky
Stanford University
Stanford, U.S.A.

mvachovs@stanford.edu⇤⇤

Sorathan Chaturapruek

gracew2@stanford.edu

sorathan@cs.stanford.edu

Stanford University
Stanford, U.S.A.

Olga Russakovsky

Richard Sommer

olga@cs.stanford.edu

sommer@stanford.edu

Stanford University
Stanford, U.S.A.

Stanford University
Stanford, U.S.A.

ABSTRACTThe field of computer science su↵ers from a lack of diversity. The Stanford Artificial Intelligence Laboratory’s Outreach Summer (SAILORS), a two-week non-residential freesummer program, recruits high school girls to computer science, specifically to Artificial Intelligence (AI). The program
was organized by graduate student and professor volunteers.
The goals of the pilot program are to increase interest in AI,
contextualize technically rigorous AI concepts through societal impact, and address barriers that could discourage 10th
grade girls from pursuing computer science. In this paper
we describe the curriculum designed to achieve these goals.
Survey results show students had a statistically significant
increase in technical knowledge, interest in pursuing careers
in AI, and confidence in succeeding in AI and computer science. Additionally, survey results show that the majority ofthe students found new role models, faculty support, and asense of community in AI and computer science.

Keywords
Artificial Intelligence, K-12 Education, Outreach, Genderand Diversity, Summer Camps, Recruiting Women, Computer Science Education

1.

INTRODUCTION

Recent diversity reports demonstrate a large gap between
the percentage of women holding computing jobs comparedto the percentage of men [1]. This disparity pervades inacademia as well: in 2011, only 17.6% of Bachelor’s degrees
and 20.2% of Doctoral degrees in computer and information
sciences from U.S. institutions were awarded to women [2].
⇤Equal contributions
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DOI: http://dx.doi.org/10.1145/2839509.2844620⇤

Grace Wu

Stanford University
Stanford, U.S.A.

Li Fei-Fei

Stanford University
Stanford, U.S.A.

feifeili@cs.stanford.edu

In this paper, we evaluate the Stanford Artificial Intelligence Laboratory’s Outreach Summer (SAILORS) as a wayof addressing the lack of diversity in Artificial Intelligence(AI). SAILORS recognizes that the AI field needs greater
diversity, and sees AI’s potential for technical and social impact education as a way to attract more women to the fieldof computer science (CS) as a whole. This potential has been
untapped in high school CS education. SAILORS is the firstAI camp for high school students that gives them a largescope overview of the humanitarian applications of the field
as well as the technical methods behind these applications.
We evaluate the pilot program, a two-week nonresidential
free1 program run by 40+ graduate student and professor
volunteers. The program focuses on gender diversity by targeting rising 10th grade girls, and has the following goals:
1) increase interest in AI among the students; 2) contextualize AI technical concepts through societal impact; and 3)
address barriers that could discourage 10th grade girls frompursuing CS in the future.
Increase interest in AI. SAILORS exposes students to
AI topics such as natural language processing (NLP) andcomputer vision, introduces these topics through technicalskills, and gives them the opportunity to have close interactions with faculty and students from the Stanford AI Lab.
Through these topics and opportunities, we aim to spark thestudents’ curiosity and motivation.
Contextualize AI through social impact. SAILORS
balances the technical skills with their application and frames
technical skills as a stepping stone towards a larger goal.
Within SAILORS curriculum, these problems have a humanitarian focus, which shows an alternative to the typesof problems the stereotypical computer scientist works on.
For example, girls chose from research projects that eachapplied AI technical skills towards a humanitarian purpose:making hospitals safer, assisting disaster relief, identifying
cancer cells through the genome, and making driving safer(see section 4.5).
Address barriers for girls in computer science. SAILORS
focuses on the gender-gap problem of recruitment within AI.
1

Funded by sponsors

The program addresses three of the barriers that women in
CS face at the undergraduate level, so that when SAILORS
alumni reach university level, they will be equipped to handle these barriers. SAILORS addresses 1) lack of exposure
to technical concepts early on [3, 4]; 2) limited perceptionof CS as a field [3, 5, 6]; and 3) lack of faculty and same-sex
peer-to-peer support [3, 6, 7].

2.

RELATED WORKS

Based on our literature review, SAILORS is unique inthree ways: 1) it is the only summer AI program in the U.S.
geared specifically towards high school girls; 2) it is the only
high school summer program in the U.S. that teaches multiple subfields of AI (instead of focusing on only one, suchas robotics [8] or bioinformatics [9]); and 3) it is the only
summer program to teach these subfields through technically rigorous and socially relevant research projects.
CS programs for girls. There exist many out-of-school
programs that target the same demographic as SAILORS;
some of these work or worked towards similar goals. Girlson the Go was a week-long residential summer program heldat Miami University for female high school sophomores andjuniors [10]. Related to our goals, the camp introduced students to computing by exposing them to fields computer scientists collaborate with. Specifically, Girls on the Go taught
girls to create an app that allows zookeepers to record animal behavior. The University of Delaware held an eightweek half-day summer program for high school girls called
Girls’ POWER [11]. The camp’s structure was similar to
SAILORS’s: the teaching was done by CS professors, thecamp introduced girls to role models in CS research and industry, and there was a programming exercise after lectures.
AI programs for minorities. Some camps targeting underrepresented groups focused on one aspect of AI. Young
Women in Computing (YWIC) was a five-week long summer
program at New Mexico State University that targeted female high-school students [9]. Like SAILORS, YWIC aimed
to increase interest, peer mentoring, and role models in CS.
The program included a two-week long bioinformatics session, where students completed research projects in the field,
e.g. investigated genes associated with particular illnesses.
Additionally, the University of Minnesota Center for Distributed Robotics and the Digital Technology Center conducted a week-long day camp geared towards middle-school
students underrepresented in CS (girls, African-Americans,
Latinos) [8]. The camp, like SAILORS, was run by graduate student volunteers, and also found success in o↵ering
students real laboratory experience through robotics lessons.
However, robotics and bioinformatics are only two topics inthe larger field of AI. SAILORS aims to introduce students
to the foundational technical concepts in AI, such as classifiers, and include topics from many concentrations in thefield.

3.

PROGRAM RECRUITMENT

We recruited students to apply for the program primarily
through email. We emailed high school teachers in the bay
area as well as students within the Stanford summer camp
database (i.e. students that had applied for a Stanford camp
before). As a pilot program, we found this method neces-sary to directly reach interested students. We welcomed
students from all income levels and backgrounds to apply.
Interested students completed a free online application composed of short response questions about their previous activities and classes, a longer question about their understanding of AI, and a teacher recommendation form. Students
submitted a transcript. By the application deadline, we received 213 applications for 24 spots in the program. Overall,
the following criteria were used throughout the admissions
process: high academic performance in math and scienceclasses, demonstrated leadership ability, enthusiasm for AI
as exhibited through responses, strong teacher recommendations, and critical thinking as exhibited through the essay.
Although previous CS experience was not a factor in admissions, all admitted students had some previous experience.
Experience ranged from self-taught to AP CS; about half ofthe students taught themselves basic CS and 20% took a CS
class in school (less than or equivalent to CS1). Our pool ofadmitted students consisted of girls passionate about technology who performed strongly in academics, but did notnecessarily have much exposure to AI. All admitted students
had high GPAs and many participated in science fairs andother extracurricular activities. A majority of them were
Asian, reflecting our applicant pool, which was roughly 75%
Asian. Additionally, all but two of the students came from
California, as SAILORS is a non-residential program.

4.

PROGRAM CURRICULUM

The two-week schedule was designed to fulfill the overall
goals of SAILORS, and introduced girls to concepts in AI
through a variety of engaging avenues. The first half of thedaily schedule introduced students to research topics in AI
and CS while the second half of the day allowed students
to have first hand experience with some of the topics. Eachactivity targeted a combination of SAILORS’s three goals,
and balanced technological theory/rigor and social impact
through AI. This balance is a part of what strongly di↵erentiates SAILORS’s curriculum from other CS camps. Because most students came in with a wide range of programming experience, the curriculum was designed assuming they
had little to no programming knowledge.

4.1

Professor Lectures

Each morning began with breakfast with a professor, followed by an hour-long professor lecture. Students enjoyedthe chance to speak personally with the professor about
their research before the formal lecture. After breakfast,
professors presented on the main topics and applications of
their research. Topics included computer vision, cognition,
biomedical informatics, robotics, information networks, natural language processing, human-robot interactions, computational sustainability, and aeronautics/astronautics. The
lectures emphasized real-world applications to excite and inform the students about the types of problems AI and CS
can solve. For example, one professor lectured on air controllers, and how he used dynamic programming to reduce
air traffic collisions while also decreasing unnecessary flightpath changes. Post-lecture breaks gave students an opportunity to interact closely with the professor, so that they may
be inspired to learn more about the topic after the lecture.

4.2

Classroom Sessions

Following the professor lecture, graduate students explained

in detail one technical component of the lecture. The graduate student instructors began with a lecture-style explanation; the students then worked on relevant problems to practice the concept. For example, one postdoctoral fellow gave
a talk on Fisher’s exact test. Students then used real datafrom a computational biology research site to compare genes
based on varying parameters. For another session, students
learned about inductive reasoning: when given a partial pattern of numbers, they guessed numbers that should belong,and compared the probability of their guesses to probabilistic programs that do the same. Other topics included nearest neighbor algorithm, correlation, graph search algorithms,
and PID controllers. The classroom sessions gave students
the confidence that they themselves can use the technicalmethods used in AI, and that they have the ability to delve
into the technical aspects of the field.

4.3

Personal Growth Sessions

The personal growth sessions aimed to give girls the skills
they need to be successful as women working in STEM.
These skills were not AI specific and gave students the ability to thrive in the face of the more general challenges—
microaggressions, working in teams, etc.—of both university
level classes and of the AI/STEM workplace. The sessions
were 1) talk with AI women; 2) team dynamics; 3) scientific communication; 4) industry panel; 5) design thinking;6) how to recognize and react to microagressions; 7) time
management; and 8) staying involved in AI/CS.
Excluding the panels, 1) and 4), the sessions were led ina seminar setting by a professional in the area. For example, a professor from Stanford Design school led the design
thinking session. The sessions transitioned between shortlectures, facilitated discussions, and interactive activities.
The panels were conducted Q&A style, and the students
asked relevant questions to the panelists.

4.4

Social HourSocial hour gave the students a space to build friendships and community outside of their project work. Twosocial hours were spent exploring technology on campus:
self-driving car and haptic surgical simulation; the remainder were unstructured.

4.5

Research Projects

A two hour time block each day was dedicated to research
projects. The students were split up into four groups of six,with two graduate students leading each team. Project topics included computer vision, robotics, NLP, and computational biology; the students ranked the topics by preference
and worked on the same project for the entire program. The
project topics were carefully selected to be engaging, to emphasize the real-world impact of AI, to be technically stimulating, and to showcase the variety and depth of problems
AI can solve. Additionally, the research projects were designed to closely mimic current AI research. Students could
thus get a first-hand experience of what researchers in thefield work on, and gain confidence that they can work on thesame. In this way, the projects aimed to increase students’
understanding of the technical methods used in AI as wellas their excitement towards research in the field.
The coding for all of the projects was done in Python
through the programming environment Jupyter. Each project
included a short general introduction to coding. During the

projects, the students implemented their code individually
but worked in pairs to brainstorm ideas.

4.5.1

Making Hospitals Safer with Computer Vision

Students learned how to develop computer vision algorithms that can automatically understand actions in videos
in order to monitor hand washing in hospitals. Their objective was to develop an algorithm that can recognize whether
or not a doctor uses the hand sanitizing dispenser before
he/she enters a room. First, to consturct a dataset, thestudents recorded a video and labeled each frame as having
used the sanitizer or not. Then, instructors presented an
overview of machine learning classifiers and introduced thebackground subtraction algorithm.
The students worked in pairs to code this algorithm. The
instructors provided simple skeleton code, guidance whenneeded, and the evaluation code with the F-score metric;
students largely coded the background subtraction algorithm
independently. Manually choosing a threshold was the mosttechnically rigorous portion. The students experimented
with the size and location of the crop region for the algorithm as well as what to threshold (color channels, image di↵erence, etc). After some manual trial and error, thestudents learned how to use the machine-learning assessment technique cross validation to automatically choose thethresholds.
In the remaining days, the students learned about andprogrammed a linear classifier. Because of limited time, thewhole group with the instructors collectively coded the classifier on the board. Then, the students implemented it separately and compared the F-score of the background subtraction algorithm and the linear classifier. They discovered thatthe linear classifier performed much better than backgroundsubtraction. The students left with a broad understandingof the full pipeline of how to train and deploy an activity
recognition model for a real-world healthcare application.

4.5.2

Assisting Disaster Relief with Natural Language
Processing (NLP)

Students learned how to use NLP to determine which areaof the world needs disaster relief, and which relief is needed(such as food, water, medical care). Students took a real
corpus of tweets made during Hurricane Sandy, and automatically determined which of the tweets were relief requests
for food. Each student built their own Naive Bayes classifier
to classify the tweets.
Students began by exploring the raw data of tweets. Using the data, they created a simple rule-based classifier by
writing the rules function in the classifier starter code. The
students saw that this classifier does not generalize well, and,as an alternative, the instructors introduced Naive Bayes
as a statistical model. For a hands-on demo, instructors
used plastic cups to represent classes, grapes to represent
instances, and grape color to represent features.
Students first used the Naive Bayes algorithm hands-on
to classify Set cards by color, pattern, and number of icons.
Using instructor starter code, they coded their algorithm.
They then revisited the tweets, and explored the pros andcons of Naive Bayes as applied to tweets. Students wrote thecode to extract features from a tweet, calculate class probabilities, calculate conditional probabilities, and predict theclass of an unseen tweet. Students ran their final programson the data, and compared the results to the rule-based clas-sifier. At the end of the two weeks, the students saw thatthey have the ability to create something that processes real
data to have a social impact.

4.5.3

Decoding DNA: Finding Meaning in the Genome

Students learned some of the technical methods used incomputational biology. Students focused on gene expression
data from several types of cancer, and used di↵erent classifiers to distinguish between normal and tumor samples, aswell as between cancer types and stages of progression.
Students first learned the high-level concept of classifiers.
As a hands-on example, students were given training grapes
and tomatoes and developed features to distinguish them.
This example intuitively demonstrated the k-nearest neighbors algorithm. After gaining the conceptual knowledge,
students explored the online dataset of genes and the performances of k-nearest neighbor algorithm on various k (withworking code written by the instructors). Then, students
programmed the k-nearest neighbor algorithm themselves,
working from instructor-written starter code. Students tested
the performance of their algorithm on various datasets (provided by the instructors) with various parameters.
Student pairs brainstormed their own final experiments.
One group tested the e↵ect of random feature selection withk-nearest neighbors classification, while another tested thee↵ects of balanced versus unbalanced datasets. By the endof the two weeks, students understood the basic biology ofcancer, as well as several computational techniques used foranalyzing cancer datasets to identify and classify tumors.
The Python code of the project, including the student’s
code, can be viewed online at goo.gl/okTkdj.

4.5.4

Self-Driving Cars and the Future of PersonalTransportation

Students implemented a basic control system for a compact robotic car to navigate a system of roads represented
by lines. Each day the instructors first presented a concept
used by real autonomous cars, and then translated the concept to their robots, Pololu m3pi. The instructors taught
three levels of control: 1) low level trajectory tracking, 2)
motion planning, 3) routing.
In the first week, students learned control for low level trajectory tracking. The students focused on the algorithms,
while communication infrastructure, data structures, etc.
were prepared by the instructors. More specifically, the students programmed a proportional-integral-derivative (PID)
controller to make a compact robotic car follow lines on asmall road network using light sensors. They then iteratively
tested the robots and adjusted control parameters and control strategies until the robot correctly and smoothly completed the task.
In the second week, students implemented Dijkstra’s algorithm in Python to find the shortest path for the car. The
students first independently wrote a function to return thecost of a path given the nodes of the path. The instructors then explained Dijkstra’s algorithm, and the students
worked in groups of two or three to implement Dijkstra’s incode. The instructors provided a package to allow the students to see a visualization of their algorithm on a graph.
Near the end of the week, students practiced route planningwith other robotic cars on the line roads, simulating realroads. Students were able to receive information about thelocations of other robotic cars and use Dijkstra’s to choose apath to avoid collisions. In addition to technical knowledge,
students gained an understanding of the potential value andsocietal impact of autonomous vehicles.
The Python code of the project, including the student’s
code, can be viewed online at goo.gl/kEWm3z.

4.6

Field Trips

The camp featured two field trips, one to the Computer
History Museum with a tour led by Turing Award winner Ed
Feigenbaum, and the other to Google to listen to a panel of
Google female engineers and have lunch with the engineers.
The first showed the students the rich history of computerscience with an emphasis on AI, as told by the “father ofexpert systems”. The second showed the students what anindustry workplace looks like and what computer scientists
in industry do on a day to day basis, with an emphasis
on women in computer science. The students thoroughly
enjoyed speaking with these professionals.

5.

EVALUATION

We administered two sets of surveys. The first set included relevant questions from [12], as well as AI-oriented
questions, and allowed us to establish a baseline for the attitudes and abilities of our admitted students. Relevant materials include [13, 14]. The second set of surveys focusedon the admitted students pre and post participation in the 2week summer program. Because the survey was AI specific,
we did not use a validated instrument.

5.1

Baseline Survey

Students were asked questions about their attitudes towards math and science, their comfort level with peers at
school, STEM career knowledge, humanitarian career interest, AI social interest, AI technical interest. For the analysis
and for all following analysis, the Likert scale was quantified from 1 to 5, where 1 is the most negative and 5 is themost positive. For robustness of analysis, the questions weregrouped based on the topic they measured. For each group,a scaled mean was calculated per participant. The resulting means suggest that the students have strong math andscience backgrounds, and are passionate in engineering, humanitarian issues, and AI (mean above 4 in these areas).
The survey also suggests students entered the program successful in general areas that SAILORS targets, for example STEM career knowledge and comfort in STEM classes(mean above 4 in these areas). For this reason, the second
survey asks more specific questions about SAILORS’s goals.
Before and after analysis was done on the baseline survey,
with the same grouping as above. 11 paired t-tests were performed on 11 of the groups. Using the Benjamini-Hochberg
procedure to control for false positive discovery rate, noneof the t-tests were statistically significant. We speculate thehigh means did not leave much space for significant increase.

5.2

Pre/Post Survey

Students entered the program expressing a strong interest in CS and AI. At the end of the 2 week program, wefound a statistically significant increase in their confidence
and likelihood to pursue CS and AI. To test for a change
in the students between the start and end of the program,paired analysis was done. The Likert scale questions were
tested using a paired t-test. There were 11 such questions(Table 1). The two questions measuring interest in AI (Q13

Table 1: Mean value changes in survey responses of the 24 SAILORS students before and after the program.
#
Question
Mean Before Mean After p-value
12
How likely is it that you will have a career in AI in the future?
3.50
4.08
.0002
3
How likely do you think it is that you attain that position [your dream
3.33
3.83
.001
computer science position]?
8
When I am learning CS, I know girls who I am comfortable asking for
3.08
3.92
.001
help.
10
How likely is it that you will study AI?
3.79
4.25
.002
15
I can succeed in AI.
4.17
4.50
.002
9
How likely is it that you will major in computer science in the future?
4.29
4.58
.005
14
I can succeed in CS.
4.33
4.58
.011
11
How likely is it that you will have a career in CS in the future?
4.17
4.46
.016
Grouped: 1) I want to learn more about AI. 2) I want to do research in
4.71
4.75
.664
Grouped AI.
13,17
16
I want to do research in CS.
4.50
4.50
1.000

and Q17) were grouped together when tested, giving a totalof 10 t-tests. The paired t-test analysis with BenjaminiHochberg procedure found that questions 3, 8, 9, 10, 11, 12,
14, and 15 show a statistically significant (↵ = .05) positive di↵erence between pre-SAILORS student answers andpost-SAILORS. Thus, there exists statistically significant
evidence that SAILORS relates to 1) increase in confidence
in CS (Q3 and Q14); 2) increase in confidence in AI (Q15);
3) increase in likelihood of pursuing CS (Q9 and Q11); 4)
increase in likelihood of pursuing AI (Q10 and Q12); and 5)
increase in same-sex peer-to-peer support (Q8).
The survey also asked technical questions to gauge thetechnical skills they learned at SAILORS (for example: “Whatis the main focus of controls engineering?”). The technicalquestions were tested using McNemar test performed as binomial exact test with Yates continuity correction. The test
uses categories incorrect and correct, paired before and after. There is a statistically significant (↵ = .05) change
between answers for three out of five of the questions. For
these three questions, we see an increase in the number ofcorrect answers from before to after. These three questions
addressed high-level technical concepts used in AI, as wellas general CS, while the other two addressed simple probability and coding. Thus, there exists statistically significant
evidence that SAILORS relates to an increase in CS and AI
technical knowledge.
Students were asked if they had advisors in CS, role models in CS, and role models in AI (other than their parents).
These questions were tested using McNemar test, with categories yes (student has advisor or role model) and no (doesnot have), paired before and after. There was a statistically significant (↵ = .05) positive change between answers
for AI role model (p = .0002). Thus, there exists statistically significant evidence that SAILORS relates to increasein AI role models. In the pre-survey, no student cited university level faculty as a CS advisor or role model. In the
post-survey, 64% of those who have CS role models cited
SAILORS-affiliated faculty, and 79% of those who have CS
advisors cited SAILORS-affiliated faculty. Thus, there also
exists statistically significant evidence that SAILORS relates
to increase in faculty role models and faculty support.
When asked to give a short answer on what kind of problems people in AI work on, students first gave high-levelanswers along the lines of solving societal problems to makehuman lives easier. After the program, students gave more
specific answers such as: “People in AI work on making models and graphs to predict or classify a range of items from
diseases to birds.” Additionally, when asked if their perception of who can be successful in CS changed and how, 83%
of students responded yes, with the most cited responses being that anyone can be successful in CS, computer scientists
can have other interests outside of CS, and other qualities
besides academic strength (teamwork, problem solving) arealso required to be successful in CS. Finally, we asked thestudents to elaborate on a problem in the world they are
passionate about and what specific technical methods in AI
can be used to solve that problem. Before the program, students cited “coding” and “robots”. We saw an increase in
the students’ knowledge of which technical methods could
be used to solve the problem. For example, one student
stated that we can use the k-nearest neighbors algorithm toclassify trash and recycling in order to prevent waste.
In addition to the questions above, we asked students for
feedback on specific components of the curriculum. When
asked to respond on Likert scale to the statements “Theprojects I built can help people” and “The projects I built
were interesting”, 95.8% of the students replied with strongly
agree. 83.3% of students strongly agreed with the statement,“I am proud of what I built” and 75% of students strongly
agreed with the statement “I am confident that I can workon a similar project in the future”. For the personal growthsessions, when asked to respond to “There are people likeme in AI”, “There are people like me in CS fields”, “I canfind support in a CS field”, and “I am comfortable speaking
up for myself in a CS field”, the students all responded withan average above 4 on the quantified Likert scale.

5.3

Limitations

Despite positive results, the pilot program had several limitations. First, the survey was unanonymized for internal
purposes; future years should anonymize the survey. Second,
the sta↵ knew of the goals, and discussed some of the goals
with the students, perhaps resulting in confirmation bias.
Third, SAILORS did not reach low-income, African American, or Latino students. Since students of these backgrounds
were not included in our study, we cannot draw conclusions
from our results towards those demographics. Based on theirapplications, our students had access to other opportunities

to learn about CS; while SAILORS may have been the only
opportunity for them to learn AI, future years may target
girls who do not have many technical opportunities.

6.

CONCLUSION

The results show a positive impact of SAILORS upon thepilot-year students. Namely, the results support achieving
goals 2 (contextualize technical AI concepts through social
impact) and 3 (address barriers for girls in CS), and some
support in achieving goal 1.
In support of goal 2, students’ perception of AI and CS
shifted to include a wider variety of people who can successfully work in the field. They also left knowing concrete,
specific technical methods that can solve the problems they
are passionate about. Research projects successfully allowed
students to technically work on real-world applications.
In support of goal 3, students gained faculty support,
same-sex peer-to-peer support, exposure to technical methods, and increased their AI and CS confidence. For faculty support, students gained faculty role models and advisors in CS and AI. For same-sex peer-to-peer support, students’ comfort asking girls for help increased, and they cited
SAILORS as a community. The personal growth sessions
successfully allowed girls to relate to each other and other
women in the field. The program has also created a community for SAILORS’s volunteer faculty at the AI lab. For
technical exposure, students were exposed to many technical methods used in AI; by the end of the program they
had built classifiers, and correctly answered questions about
technical methods used in AI. For confidence, students left
feeling more likely to succeed in both AI and CS, as well
as more likely to go into both fields. Students were alsoproud of their research projects, and their ability to engage
real-world applicable technical methods.
The results do not directly show support for increase ininterest. We speculate students already came in with a very
high level of interest in AI (Likert scale mean 4.7/5) becauseof our admissions criteria. However, the increase in likelihood to go into AI suggests a potential increase in longer
term interest.
All in all, SAILORS was successful this year in achieving
its short-term goals. Our hope now is that the excitement
and confidence the girls feel about AI and CS, as well as thecommunity they have found, persist. To facilitate, we plan
to hold events with both the students and AI faculty. Ultimately, SAILORS’s pilot year has proven to be an e↵ective
way to educate students in AI and the societal impact of
CS. In future years, we hope to reach a more diverse demographic of students as well as see the long term impact ofthe program.

7.

ACKNOWLEDGEMENTS

We are grateful to all of the SAILORS sta↵, students,
generous sponsors, and anonymous reviewers.

8.

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