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Confidential Treatment Requested by Arrowroot
Acquisition Corp.

Certain confidential information in this letter
has been omitted and provided separately in an unredacted version to the Securities and Exchange Commission. Confidential treatment has
been requested pursuant to 17 C.F.R. Section 200.83 with respect to the omitted portions, which are identified in this letter as filed
via EDGAR with a placeholder identified by the mark “[***].”

    Goodwin Procter LLP

    100 Northern Avenue

    Boston, MA 02210

    goodwinlaw.com

    +1 617 570 1000

December 7, 2023

BY EDGAR

Division of Corporation Finance

Office of Technology

U.S. Securities and Exchange Commission

100 F Street, NE

Washington, D.C. 20549-3628

    Attention:
    Amanda Kim

    Stephen Krikorian

    Charli Gibbs-Tabler

    Jan Woo

    Re:
    Arrowroot Acquisition Corp.

    Registration Statement on Form S-4

    Originally Filed September 5, 2023

    Amendment No. 1 to Registration Statement on Form S-4

    Filed November 6, 2023

    File No. 333-274333

Ladies and Gentlemen:

This letter is submitted on behalf of Arrowroot
Acquisition Corp. (the “Company”) in response to comments from the staff of the Division of Corporation Finance
(the “Staff”) of the Securities and Exchange Commission in a letter dated November 29, 2023 (the “Comment
Letter”) with respect to the above-referenced Registration Statement on Form S-4 initially filed on September 5, 2023, as
amended by the Amendment No. 1 to Registration Statement on Form S-4 filed on November 6, 2023 (the “Registration Statement”).
The Company is concurrently submitting Amendment No. 2 to the Registration Statement (the “Amendment No. 2”),
which includes changes in response to certain of the Staff’s comments.

For your convenience, the Staff’s numbered
comments set forth in the Comment Letter have been reproduced in bold with responses immediately following each comment. Unless otherwise
indicated, page references in the descriptions of the Staff’s comments refer to the Registration Statement, and page references
in the responses below refer to Amendment No. 2. Defined terms used herein but not otherwise defined herein have the meanings given to
them in Amendment No. 2.

The responses provided herein are based upon information
provided to Goodwin Procter LLP by the Company.

Page 2

Amendment No. 1 to Registration Statement on
Form S-4

Customers, page 201

    1.
    You have defined and disclosed the number of contracted customers and licensed users for the periods presented, as well as defined enterprise end customers. Please disclose the number of enterprise end customers for the periods presented.

Response: The Company respectfully acknowledges the
Staff’s comment and advises the Staff that iLearningEngines does not manage or monitor its business based on the number of enterprise
end customers, as such number varies in a fiscal period due to changes in the mix of solutions used by iLearningEngines’ contracted
customers. In addition, iLearningEngines’ enterprise end customers include iLearningEngines’ VARs’ customers. Accordingly,
iLearningEngines does not track enterprise end customer growth on a quarterly basis because enterprise end customers count by quarter
is too difficult to determine with reasonable certainty within reporting timeframes.

iLearningEngines monitors and intends to disclose recurring
revenue growth, contracted customers growth and licensed user growth because iLearningEngines believes they are better indicators of iLearningEngines’
business and consistent with the ways in which iLearningEngines monitors its business internally.

iLearningEngines Management’s Discussion and Analysis of Financial
Condition and Results of Operations

Key Performance Metrics, page 220

    2.
    Your response to prior comment 9 explains that Annual Recurring Revenue and Net Dollar Retention helps provide information as to the performance of iLearningEngines’ recurring subscription revenue base and impact of revenues from existing customers. However, your disclosure on page 221 explains that the ability to attract and engage new customers is also one of the key factors affecting your performance. In light of this disclosure, please tell us what consideration was given in disclosing the number of customers for the periods presented by new and existing customers. Refer to SEC Release No. 33-10751.

Response: The Company respectfully acknowledges the
Staff’s comment and advises the Staff that it has revised the disclosure on page 203 of Amendment No. 2 to include the number
of new contracted customers for each period presented in response to the Staff’s comment. The Company further advises the Staff
that it has not included a breakdown of new and existing licensed users because iLearningEngines frequently upsells new use cases inside
enterprises (e.g. iLearningEngines implements a new site/instance for different departments or businesses within an enterprise). As such,
tracking unique licensed user count changes are too difficult to determine with reasonable certainty within reporting timeframes.

    3.
    Your response to prior comment 11 and revisions to the disclosures on page 220 explain that you do not exclude prior year contracted customers that were not retained in the current year. However, your response to prior comment 9 explains that Net Dollar Retention helps provide information as to the performance of iLearningEngines’ recurring subscription revenue base and impact of revenues from existing customers. Please tell us how including prior year contracted customers that were not retained in the current year provides useful information on existing customers given that those customers have not been retained.

Response: The Company respectfully acknowledges the
Staff’s comment and advises the Staff that iLearningEngines includes prior period contracted customers that were not retained in the
current year in the calculation of Net Dollar Retention as this formulation captures the impact of any customer churn. iLearningEngines
believes this metric provides useful information about its overall ability to retain and grow its customer base while
reflecting the impact of any customer losses. The Company believes this definition of Net Dollar Retention provides investors with a more
fulsome picture of iLearningEngines’ customer relationships than a definition without the effect of customer losses would provide.

Page 3

Comparison of Six Months Ended June 30, 2023 and 2022, page 225

    4.
    In response to prior comment 15, you have revised your disclosure to state that revenue increased due to thirteen new contracts. However, your disclosure continues to state that the cost of revenue increased due to fourteen new contracts. Please revise or advise.

Response: The Company respectfully acknowledges the
Staff’s comment and advises the Staff that it has revised the disclosure on pages 227, 230 and 232 of Amendment No. 2 to discuss
changes in global revenue prior to a discussion of changes by region. This change allows the reader to reconcile the total change in number
of contracts listed by region to those discussed on a global basis in the cost of revenue section.

Notes to Consolidated Financial Statements

Combined software license and maintenance, page F-15

 5. Your response to prior comment 18 states that you determine
SSP for the combined software license and maintenance performance obligation using the residual approach because iLearningEngines sells
the iLearningEngines AI platform and related maintenance services to different customers for a broad range of amounts, such that there
is not a discernible standalone selling price from past transactions. Please provide a comprehensive, quantitative discussion of such
variability to support your conclusion. As part of your response, please quantify the amount of revenue recognized for where the residual
method is used. Refer to ASC 606-10-32-34.

Response: The Company respectfully
acknowledges the Staff’s comment and advises the Staff that sales of the iLearningEngines AI platform involve the transfer to the
customer of a combined software license and maintenance performance obligation, the standalone selling price (“SSP”)
of which is determined using the residual approach. Under ASC 606-10-32-34(c), an entity may use the residual approach to estimate SSP
by referencing the total transaction price less the sum of the observable standalone selling prices of other goods or services promised
in the contract. The residual approach may only be used if one of the following criteria is met:

 1. The entity sells the same good or service to different customers (at or near the same time) for a broad
range of amounts (that is, the selling price is highly variable because a representative standalone selling price is not discernable from
past transactions or other observable evidence).

 2. The entity has not yet established a price for that good or service, and the good or service has not previously
been sold on a standalone basis (that is, the selling price is uncertain).

iLearningEngines has concluded that the selling price of the combined
software license and maintenance performance obligation is highly variable and therefore that the use of the residual approach to estimate
the SSP of the combined software license and maintenance performance obligation is appropriate.

When iLearningEngines sells its AI platform and related maintenance
services to customers, it presents the price of the license and maintenance to the customer by quoting both a price  per user per month
and per expert per month. There are a number of factors that affect the per user and per expert prices charged to different customers
including, but not limited to, the customer’s bespoke products which the AI platform is replacing, the complexity of the use case
for which the AI platform is meant to solve, the number of customer systems into which the platform is integrated, the number of dedicated
support personnel required to provide maintenance services, and the outcome of contract negotiations with the customer. Due to the novelty
of AI products, iLearningEngines does not have an existing price list or competitor pricing to benchmark its pricing against. Rather,
iLearningEngines creates a per user and per expert price customized to the customer’s specific use case and the customer’s
budget. The high degree of variability in the per user and per expert prices per month charged to different customers served as the basis
for using the residual approach.

Page 4

iLearningEngines’ evaluation of whether the pricing for the license
and maintenance is highly variable was primarily a quantitative approach involving an analysis of its license and maintenance pricing
across all active contracts from 2018 to 2023 (inclusive of renewals), as iLearningEngines has a history of charging different customers
a broad range of amounts for the license and maintenance. iLearningEngines considered the discussion in the American Institute of Certified
Public Accountant’s Audit & Accounting Guide, Revenue Recognition, Chapter 9—Software Entities, paragraph 9.4.05:

In order to use the residual approach
for the software license in the contract, an entity will first need to evaluate whether the software license sold to the customer is the
“same” software sold to other customers and whether the selling price of the same license has been highly variable or uncertain
in other transactions.

With respect to whether the software license is the “same”
software licensed to other customers, iLearningEngines considered the various factors noted in paragraph 9.4.06, i.e.,

whether the license is perpetual or
time-based (and, if time-based, the duration of the license term, for example, three years versus seven years), exclusive or nonexclusive,
or restricted regarding permitted uses.

iLearningEngines noted that the platform is typically licensed to customers
for multi-year terms and that there were no significant differences in license attributes from customer to customer. iLearningEngines
further considered whether any stratification of the customer population was appropriate, noting that ASC 606-10-32-32 states that the
“best evidence of a standalone selling price is the observable price of a good or service when the entity sells that good or service
separately in similar circumstances and to similar customers” (emphasis added). iLearningEngines sells its AI platform
to customers in several different industry sectors, but it did not identify significant differences in pricing characteristics when examining
pricing data by customer industry sector. iLearningEngines also analyzed whether stratifying its customer population by channel contracts,
those customers contracted through iLearningEngines’ channel partners (“Channel Contracts”), and direct
contracts, those customers who have contracted directly with iLearningEngines (“Direct Contracts”), would result
in differences in the variability of pricing. Channel Contracts are those contracts in which a channel partner is iLearningEngines’
customer, while Direct Contracts are those contracts in which the end customer is iLearningEngines’ customer. There was not a significant
difference in the variability of pricing between Channel Contracts and Direct Contracts. However, iLearningEngines determined that presenting
its analysis on the basis of all contracts, as well as Channel Contracts and Direct Contracts, would be useful for illustrative purposes.

Accordingly, the tables below reflect iLearningEngines’ analysis
of pricing on a per user and per expert pricing per month, with customer contracts stratified by (1) Channel Contracts and (2) Direct
Contracts. The analysis was performed using a population of all past and current customer contracts. iLearningEngines utilized the bell-shaped
curve approach to examine the per user and per expert per month pricing in its contracts by identifying a median price in the relevant
population, then determining whether prices are sufficiently clustered within a narrow range.

Page 5

iLearningEngines considered the following discussion in Question 6-3
in EY’s Financial Reporting Developments publication, Revenue from contracts with customers (ASC 606) (issued September 2023),
with respect using a range of observed prices to determine a standalone selling price:

An estimate of the standalone selling
price could be established when a large portion of the standalone sales fall within a narrow range (e.g., when the entity could demonstrate
that the pricing of 80% of the standalone sales fall within a range of plus or minus 15% from the midpoint of the range), since this approach
is consistent with the standard’s principle that an entity must maximize its use of observable inputs.

While the use of a range may be appropriate
for estimating the standalone selling price, we believe that some approaches to identifying this range do not meet the requirements of
the guidance. For example, it wouldn’t be appropriate for an entity to determine a range by estimating a single price point for
the standalone selling price and then adding an arbitrary range on either side of that point estimate or by taking the historical prices
and expanding the range around the midpoint until a significant portion of the historical transactions fall within that band.

To illustrate, assume that an entity
determines that 60% of its historical prices fall within +/-15% of [***] (i.e., [***] to [***]). However, the entity determines that 80% of
the historical prices fall within +/- 30% of [***] and proposes a range for the standalone selling price estimate of [***] to [***]. The wider
the range necessary to capture a high proportion of historical transactions, the less relevant the range is in terms of providing a useful
data point for estimating standalone selling prices.

iLearningEngines’ ap