<?xml version="1.0" encoding="UTF-8"?>
<reference anchor="I-D.srivastava-learnlog" target="https://datatracker.ietf.org/doc/html/draft-srivastava-learnlog-01">
   <front>
      <title>learnlog: A Verifiable Event Log Format for Learning Software</title>
      <author initials="K." surname="Srivastava" fullname="Kumar Srivastava">
         <organization>Quantum Learning Machines</organization>
      </author>
      <date month="October" day="12" year="2026" />
      <abstract>
	 <t>   This document defines learnlog, a log format for learning software.
   A record holds typed events that describe what a learner did and what
   other parties, human or software, did in response.  As AI
   participates in learning and assessment, these records can preserve
   the assistance and producing instruments needed to interpret a
   performance.  The format does not establish independent learning or
   the effectiveness of an intervention.  Entries are chained by hash,
   and a producer can sign the chain.  A reader who trusts the signing
   key can then detect changes to the entries under a signed head that
   were made without that key.  Some changes remain undetectable, and
   the security considerations list them.  An event can be redacted, and
   the rest of the record still verifies.  The format defines files,
   records, entries, events, and registries for event schemas.  Subject-
   area event schemas and judgments about the applicability of research
   are out of scope.

	 </t>
      </abstract>
   </front>
   <seriesInfo name="Internet-Draft" value="draft-srivastava-learnlog-01" />
   
</reference>
