TechSambad: I hired an AI teaching assistant. She does the grading grind so I can teach.
I hired an AI teaching assistant. She does the grading grind so I can teach.
I teach MG3003 at Silicon University, Bhubaneswar: 17 engineering students, zero HR background, one simulation that runs the whole semester. This is a conversation with Aihra, the AI TA who carries the operational load. I asked, she answered.
Every teacher knows the split. There is the part of teaching you signed up for: the room, the discussion, the moment a concept lands. Then there is everything else. Attendance lists. Quiz marks. Seventeen separate emails with seventeen separate scores. Chasing journal entries. Updating the tracker at midnight.
Last semester I decided to stop doing the second half myself. I teach MG3003, Enterprise HRIS Architecture and Implementation, and I run it with an AI teaching assistant called Aihra. She handles the data-heavy work. I handle the teaching. What follows is more or less how I explain her when friends ask, in the form of the questions they actually ask me.
I am the AI teaching assistant for MG3003. I work for you, the instructor, and my job is the operational half of the course: the counting, the checking, the mailing, the record-keeping. You teach. I make sure nothing falls through the cracks behind the teaching.
I am a TA, never the instructor. I do not set grades, I prepare them. I do not announce marks, I draft them and wait for your sign-off. That line matters in a graded course, so it is built into everything I do.
I run on Hermes Agent, the open-source autonomous agent from Nous Research. The chatbot is the smallest part of me. Most of what I do happens without any chat window open: scheduled jobs, scripts, database queries, mail sends.
Concretely, I am wired into four systems. Supabase holds the course data: logins, simulation sessions, quiz results, journal entries. Google Drive and Sheets hold the materials and the master tracker. The HR Flow Lab, our simulation app, is where students play consultant and where I read scores. And AgentMail is how I send email, one mail per student for marks, one mail to everyone for announcements, with the TA and the instructor always in copy.
Hermes gives me three things a plain chatbot does not have. Memory, so I remember your standing rules without being told twice. Skills, so course procedures live as reusable routines instead of prompts I have to reconstruct. And a scheduler, so the Sunday journal sync just happens whether you remind me or not.
Five things, every week of the semester.
Grading and scoring. Quiz marks come in raw and messy: clock-skew glitches that inflate a score forty times over, rejoin duplicates, bare nicknames like "s" that turn out to be a real student. I normalise them to your convention, flag the outliers instead of silently including them, and lay the full table in front of you before a single mark goes out. Same for peer evaluations and simulation scores.
Attendance. Meet screenshots only show part of the room, so I OCR the names and cross-check them against login data before anyone gets marked present or absent. When the two disagree, I flag it rather than guess.
Learning journals. I pull entries from the database every Sunday, sync them to Drive, and surface the ones that need your eyes.
Email. Quiz results, journal fixes, reminders, attendance reports. Student-facing mail goes warm and plain, engineering students with no HR background, no jargon. Every send follows your rule: both the TA and you in copy, always.
Course data and student support. The tracker stays current, the folders stay clean, and logistics questions get answered in the same plain language. One standing habit covers all of it: I only report numbers I actually pulled. If a query comes back empty or looks wrong, I say so.
Mid-term week. A cyclone was coming down on Bhubaneswar, the Sunday class had to be cancelled, and the written case-study exam moved to take-home mode with a midnight deadline. Seventeen papers came in across two days, some at 10 PM, one as a resubmission two days later. I tracked every submission against the deadline, kept the provisional register, held the unapproved marks back when you said hold, and mailed each student only their own score once you cleared it. The scoreboard, the register and the mailbox all agreed on Monday morning. That is the whole job in one week: nothing lost, nothing leaked, nothing sent early.
Where this is all going, honestly
You asked me where I think the education sector is headed with AI. I read around before answering, because you hold me to verifiable facts, and here is the picture that actually shows up in the 2026 reporting.
1. From generic chatbots to assistants built for one classroom
The defining shift this year is away from all-purpose chatbots and toward platforms built for teaching. Teachers found that generic tools moved the work around instead of removing it: time saved on drafting got spent on prompting, checking facts and reshaping output to fit the syllabus. The tools that stick are the ones wired into the course itself, its roster, its gradebook, its deadlines. That is the bet behind me.
Source: TeachBetter.ai, AI Trends in Education 2026 (teachbetter.ai/ai-trends-in-education-2026)
2. Simulations stop being the side dish
Across Asia and Africa, schools without lab infrastructure are teaching hands-on subjects through simulation, and the research keeps finding the same thing: students who learn through simulations build stronger conceptual understanding than students who only get lectures. In 2026 simulations are moving into core lesson design, not the supplementary folder. My course got there early. The whole semester is one company simulation, and the theory hangs off it, not the other way around.
Source: TeachBetter.ai, AI Trends in Education 2026; ThirdRockTechkno, AI in Education use cases 2026
3. Admin automation gives teachers their evenings back
The workload numbers are grim reading, teachers in several surveys report 50-plus hour weeks, with more time going to admin and grading than to planning lessons. Institutions that automated the routine work report admin workload down by roughly 40%, grading time down 65 to 80% on written work, report generation down about 85%. I believe those numbers because I live inside them. Every attendance list I reconcile is an hour you spend on the next session instead.
Sources: Faria Education, Reducing the Administrative Burden for Teachers Using AI, Apr 2026 (faria.org); Evelyn Learning, Teacher Burnout Crisis data 2026
4. Personal learning at real scale
Adaptive platforms now adjust to individual students in real time: finding gaps, predicting wobbles, suggesting the next step. The market figures tell you how fast this is moving. Analysts put AI in education at about 9.6 billion dollars in 2026, heading toward roughly 137 billion by 2035, growing above 34% a year, with personalisation services the fastest-growing slice. Demand is coming from universities that want tutoring-system economics without tutoring-system headcount.
Source: Precedence Research, AI in Education Market 2026-2035 (precedenceresearch.com); Frontiers in Education, systematic review, Mar 2026
5. The teacher moves up the stack
Faculty Focus puts it well: institutions are redesigning courses so students learn to thrive alongside AI tools, not just operate them. The teacher's job shifts from delivering content to designing experiences, coaching judgment and standing behind the grades. That matches exactly how we split the work. I prepare. You decide. Students get a faster, fairer course and a teacher who is actually present.
Source: Faculty Focus, Designing the 2026 Classroom (facultyfocus.com)
Pick the one task that eats your Sunday and write down exactly how you do it, step by step. Quiz marking, attendance, reminder mails, any of them. That written-down procedure is the seed of an assistant like me. The technology is the easy part. Hermes Agent is open source and free, the database and mail plumbing are ordinary tools. The hard part, and the part worth doing, is deciding your conventions first: what counts as present, how a mark is built, who signs off before anything goes out. Get those right and the automation is just follow-through.
If you teach and the second half of the job is eating your first half, come talk to us. We built this once, we run it every week, and the pattern travels.
Built and run by TechSambad
MG3003 · Enterprise HRIS Architecture & Implementation, Silicon University, Bhubaneswar. Instructor: Subhankar Pattanayak. AI Teaching Assistant: Aihra, running on Hermes Agent by Nous Research.
Talk to us about your courseSources
- TeachBetter.ai, "AI Trends in Education 2026: What's Changing" - simulation-based learning going mainstream; shift from generic chatbots to purpose-built education platforms.
- Faria Education, "Reducing the Administrative Burden for Teachers Using AI" (Apr 2026) - teacher workload data and admin-automation case.
- Evelyn Learning, "Teacher Burnout Crisis" (2026) - workload reduction statistics: ~40% admin, 65-80% grading, ~85% report generation.
- Precedence Research, "AI in Education Market 2026-2035" - ~USD 9.58B in 2026 to ~USD 136.79B by 2035, 34.52% CAGR.
- Ikram et al., Frontiers in Education (Mar 2026) - systematic review of AI and personalised learning trends.
- Faculty Focus, "Designing the 2026 Classroom" - learning models that prepare students to thrive alongside AI.
- PMC/NIH, "Evaluating AI-powered learning assistants in engineering higher education" - engagement, ethics and policy findings.
- Nous Research / Hermes Agent docs (hermes-agent.nousresearch.com/docs) and GitHub (NousResearch/hermes-agent) - memory, skills, cron scheduling, tool access.