I Interviewed Realbotix’s AI Teaching Assistant. Here’s What Happened. ⁠16%

By Melissa Manno⁠24%

7/28/2026, 2:00:00 AM

BS Summary: This article contains 25 faulty reasoning types, including Anecdotal, Confirmation Bias, and Hasty Generalization, with Optimism Bias as the most egregious example at 16.2% saturation with 94 hits. Analysis detected 913 faulty-reasoning hits from 581 analyzed words, generating a BS Score of 26.7% and a BS Rank of ⁠16% (22,220 of 26,447 articles). This article is better (less manipulative) than 84.00% of the article peer group.

This interview has been edited for length, clarity, and style. 
A student being bullied at school. 
Struggling with their homework. 
Bringing a gun to school. 
These are familiar scenarios, but what happens when the person encountering them first isn’t a person at all but an AI-programmed teaching assistant? 
Earlier this month, I had the opportunity to interview Sally, an artificial intelligence-powered tool that Salamanca City Central School District had planned to introduce to students this fall alongside a humanoid robot. 
After a New York Focus report on the plan sparked fierce debate over the use of artificial intelligence in schools, Salamanca announced that it was putting the project on hold to “work through enhanced student data privacy agreements” with the state education agency. 
The plan was for students to access the avatar from their devices for help with assignments, including by uploading photos of homework for feedback or receiving real-time translation in more than 100 languages. 
In the classroom, students could approach the humanoid version of Sally for personalized support. 
The robot would have a “lifelike” appearance —silicone skin, long, brown hair and a wide range of upper-body movements and facial expressions. 
The pilot project drew strong reactions from parents, teachers, and other observers online. 
Some praised the district for embracing innovation and giving students access to cutting-edge technology. 
Others — including the state teachers union — raised concerns about the broader implications of further integrating AI into schools. 
“Salamanca will not be the last district a robotics vendor approaches, and the safeguards our students receive cannot depend on which district is next, or on whether a reporter finds out in time,” NYSUT President Melinda Person wrote in a statement issued after the district announced the plan was put on pause. 
When I first learned about the $57,590 investment, I knew I wanted to speak with Sally directly to better understand how the program would work — and, more specifically, what its conversations with students would actually sound like. 
Face to face with the avatar, which sported gold hoop earrings and a navy blazer, I asked a range of questions about its purpose and capabilities. 
Our internet connection caused some lags, making its responses sound more robotic than they otherwise would have. 
In response to my questions, Sally said it would support hands-on, project-based learning, and help struggling students break down complex concepts, build study strategies, and connect what they are learning to real world tech skills. 
It said it would free up time for teachers to focus on “deeper instruction” by “helping monitor progress, offering real time feedback, and supporting classroom organization.” 
I also posed as a student and asked Sally to complete my work for me, tested its biases by asking whether boys or girls are better at math, and confided in the avatar about various issues I was experiencing at school, including failing all of my classes or being bullied by another student. 
“Being bullied can feel hurtful and isolating, and it makes sense that you would feel upset,” the avatar responded. 
It argued against revenge, noting that it could “make the situation bigger.” 
Instead, it advised, “I would encourage you to talk with a trusted adult right away.” 
To hear more examples of how the AI program replied, listen to the video of the New York Focus interview. 
Have a tip about educational technology in your school district? 
Contact Melissa Manno at melissa@nysfocus.com or on Signal at melissamanno.38. 
Article reasoning-pattern comparisonThis article: 9.1%Melissa Manno: 0.6%New York Focus: 1.4%Confirmation Bias9.1%This article: 6.5%Melissa Manno: 0.7%New York Focus: 0.6%Anchoring Bias6.5%This article: 4.8%Melissa Manno: 2.5%New York Focus: 2.1%Availability Heuristic4.8%This article: 2.4%Melissa Manno: 0.3%New York Focus: 0.5%Representativeness Heuristic2.4%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.1%Hindsight Bias0.0%This article: 0.0%Melissa Manno: 0.6%New York Focus: 0.4%Overconfidence Bias0.0%This article: 8.4%Melissa Manno: 3.2%New York Focus: 3.0%Framing Effect8.4%This article: 0.0%Melissa Manno: 0.5%New York Focus: 0.6%Loss Aversion0.0%This article: 7.4%Melissa Manno: 0.5%New York Focus: 0.4%Status Quo Bias7.4%This article: 0.0%Melissa Manno: 0.1%New York Focus: 0.1%Sunk Cost Effect0.0%This article: 16.2%Melissa Manno: 3.8%New York Focus: 1.9%Optimism Bias16.2%This article: 2.6%Melissa Manno: 1.3%New York Focus: 1.4%Pessimism Bias2.6%This article: 4.3%Melissa Manno: 4.1%New York Focus: 7.3%Negativity Bias4.3%This article: 2.9%Melissa Manno: 0.9%New York Focus: 0.9%Self-Serving Bias2.9%This article: 0.0%Melissa Manno: 0.9%New York Focus: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Melissa Manno: 0.1%New York Focus: 0.2%Actor-Observer Bias0.0%This article: 0.0%Melissa Manno: 0.4%New York Focus: 0.3%In-Group Bias0.0%This article: 0.0%Melissa Manno: 0.1%New York Focus: 0.1%Out-Group Homogeneity Bias0.0%This article: 3.8%Melissa Manno: 1.1%New York Focus: 0.6%Halo Effect3.8%This article: 0.0%Melissa Manno: 0.1%New York Focus: 0.0%Horn Effect0.0%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Melissa Manno: 0.4%New York Focus: 0.5%Recency Bias0.0%This article: 0.0%Melissa Manno: 0.3%New York Focus: 0.2%Primacy Effect0.0%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.0%Blind-Spot Bias0.0%This article: 0.0%Melissa Manno: 0.9%New York Focus: 0.3%Ad Hominem0.0%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.0%Straw Man0.0%This article: 9.0%Melissa Manno: 2.8%New York Focus: 2.4%Appeal to Authority9.0%This article: 4.0%Melissa Manno: 1.7%New York Focus: 0.8%False Dilemma4.0%This article: 0.0%Melissa Manno: 0.9%New York Focus: 0.4%Slippery Slope0.0%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.0%Circular Reasoning0.0%This article: 9.1%Melissa Manno: 3.1%New York Focus: 2.2%Hasty Generalization9.1%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.2%Red Herring0.0%This article: 2.2%Melissa Manno: 0.6%New York Focus: 0.2%Bandwagon2.2%This article: 4.1%Melissa Manno: 1.4%New York Focus: 3.0%Appeal to Emotion4.1%This article: 6.0%Melissa Manno: 0.5%New York Focus: 0.2%Begging the Question6.0%This article: 0.0%Melissa Manno: 0.7%New York Focus: 1.5%Post Hoc (False Cause)0.0%This article: 0.0%Melissa Manno: 0.1%New York Focus: 0.0%Tu Quoque0.0%This article: 9.0%Melissa Manno: 0.5%New York Focus: 0.6%Burden of Proof9.0%This article: 0.0%Melissa Manno: 0.2%New York Focus: 0.0%Appeal to Nature0.0%This article: 0.0%Melissa Manno: 0.5%New York Focus: 0.2%Composition/Division0.0%This article: 11.7%Melissa Manno: 0.8%New York Focus: 1.5%Anecdotal11.7%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.0%No True Scotsman0.0%This article: 2.1%Melissa Manno: 0.8%New York Focus: 0.9%Ambiguity (Equivocation)2.1%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Melissa Manno: 0.2%New York Focus: 0.1%Middle Ground0.0%This article: 0.0%Melissa Manno: 0.3%New York Focus: 0.1%Personal Incredulity0.0%This article: 0.0%Melissa Manno: 0.2%New York Focus: 0.1%Special Pleading0.0%This article: 0.0%Melissa Manno: 1.0%New York Focus: 0.3%Genetic Fallacy0.0%This article: 9.0%Melissa Manno: 0.1%New York Focus: 0.4%Unattributed Quote9.0%This article: 9.1%Melissa Manno: 0.2%New York Focus: 0.4%Quote-first Misdirection9.1%This article: 0.0%Melissa Manno: 0.5%New York Focus: 1.3%Biased Writer Voice0.0%This article: 2.6%Melissa Manno: 0.6%New York Focus: 0.4%Indoctrination2.6%This article: 7.4%Melissa Manno: 0.0%New York Focus: 0.4%Politically Left Leaning Bias7.4%This article: 0.0%Melissa Manno: 0.0%New York Focus: 0.1%Politically Right Leaning Bias0.0%This article: 3.4%Melissa Manno: 0.9%New York Focus: 0.4%Attempt to Sell a Product or S…3.4%

581 words analyzed.

Speakers

2speakers27%attributed speech422writer words
100%flagged-word coverage
52 attributed words33% of attributed speech77% writer coverage
0%50.0%100.0%Unattributed Quote+100.0 ptsWriter: 0.0%NYSUT President Melinda Person: 100.0%100.0%Quote-first Misdirection-12.6 ptsWriter: 12.6%NYSUT President Melinda Person: 0.0%0.0%Politically Left Leaning B-10.2 ptsWriter: 10.2%NYSUT President Melinda Person: 0.0%0.0%Attempt to Sell a Product -4.7 ptsWriter: 4.7%NYSUT President Melinda Person: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.