Computer system and method for automated generation of instructional sequences
Methods, devices, and processor-readable media for automated generation of instructional sequences. Instructional source materials are received and parsed to extract content segments. Constrained prompts are constructed by inserting pre-approved summaries and topic-specific metadata with applied tag constraints. Responsive to the constrained prompts, remediation outputs including summaries and candidate question-and-answer (Q&A) records are generated under the tag constraints. The summaries and candidate Q&A records are presented for approval. Upon approval, a remediation plan is generated based on the tag constraints that bind a question step to a nearest preceding summary, the remediation plan comprising step metadata referencing the approved Q&A records. The remediation plan and associated content are transmitted to a user device.
1 . A computer-implemented method for generation of instructional sequences, comprising:
receiving instructional source materials;
parsing the instructional source materials to extract content segments;
constructing constrained prompts comprising pre-approved summaries and topic-specific metadata based on the content segments and applying tag constraints, wherein the constrained prompts are constructed by populating a prompt template that restricts free-form instructions and admits tag-based approved summaries;
obtaining, responsive to the constrained prompts, remediation outputs comprising at least summaries and candidate question-and-answer (Q&A) records generated under the tag constraints by transmitting the summaries and the candidate Q&A records to a user interface for edit and approval before migration from a staging schema into an approved repository;
presenting the summaries and candidate Q&A records for approval;
generating, upon approval and based on the tag constraints that bind a question step to a nearest preceding summary, a remediation plan having step metadata referencing the approved Q&A records; and
transmitting the remediation plan and associated content to a user device.
2 . The method of claim 1 , further comprising storing the summaries with display-only text elements using schema markers.
3 . The method of claim 1 , wherein parsing comprises applying optical character recognition to images or scanned documents and, for video materials, generating a transcription and keyframe screenshots, and associating the content segment with metadata including source type and at least one of a page number, section marker, timestamp, or anchor tag.
4 . The method of claim 1 , wherein presenting the candidate Q&A records for approval comprises hashing the candidate Q&A records for deduplication and staging the hashed records, and in response to an approval action, migrating a staged candidate Q&A record from the staging schema to an active schema and assigning a version token that is incremented upon subsequent faculty edits.
5 . The method of claim 1 , wherein the tag constraints comprise a plurality of hierarchical tiers including at least a first tier, a second tier, and a third tier, the tiers defining progressively granular identifiers for associating instructional content and performance data, wherein executing the remediation plan further comprises applying a tier-specific rule object comprising a time window and a score threshold to performance data tagged to the plurality of hierarchical tiers, and conditionally including one or more steps responsive to whether the score threshold is satisfied within the time window.
6 . The method of claim 1 , wherein execution of the remediation plan includes generating, for a question step, a compact evaluation prompt comprising an approved reference answer associated with an approved Q&A record, wherein the method further includes limiting permitted attempts and, in response to an incorrect attempt, appending progressively specific hint directives such that feedback is generated from the approved reference answer and an optional approved summary without disclosing the approved reference answer.
7 . The method of claim 1 , wherein constructing the constrained prompts further comprises caching static summary portions and reusing a cache identifier in subsequent prompts to reduce token transmission, and determining a token count for an assembled prompt prior to inference and, in response to the token count exceeding a budget threshold, selecting a reduced set of approved summaries and the candidate Q&A records for inclusion in the assembled prompt.
8 . The method of claim 1 , wherein the summaries are stored in dual formats including display-optimized markup and token-optimized text.
9 . A computer system comprising one or more processors and one or more memories storing machine-executable instructions thereon which, when executed by the one or more processors, cause the computer system to perform a set of processing operations to generate one or more examination questions, the processing operations including:
receiving instructional source materials;
parsing the instructional source materials to extract content segments;
constructing constrained prompts comprising pre-approved summaries and topic-specific metadata based on the content segments and applying tag constraints, wherein the constrained prompts are constructed by populating a prompt template that restricts free-form instructions and admits tag-based approved summaries;
obtaining, responsive to the constrained prompts, remediation outputs comprising at least summaries and candidate question-and-answer (Q&A) records generated under the tag constraints;
presenting the summaries and candidate Q&A records for approval by transmitting the summaries and the candidate Q&A records to a user interface for edit and approval before migration from a staging schema into an approved repository;
generating, upon approval and based on the tag constraints that bind a question step to a nearest preceding summary, a remediation plan having step metadata referencing the approved Q&A records; and
transmitting the remediation plan and associated content to a user device.
10 . The system of claim 9 , further comprising storing the summaries with display-only text elements using schema markers.
11 . The system of claim 9 , wherein parsing comprises applying optical character recognition to images or scanned documents and, for video materials, generating a transcription and keyframe screenshots, and associating the content segment with metadata including source type and at least one of a page number, section marker, timestamp, or anchor tag.
12 . The system of claim 9 , wherein presenting the candidate Q&A records for approval comprises hashing the candidate Q&A records for deduplication and staging the hashed records, and in response to an approval action, migrating a staged candidate Q&A record from the staging schema to an active schema and assigning a version token that is incremented upon subsequent faculty edits.
13 . The system of claim 9 , wherein the tag constraints comprise a plurality of hierarchical tiers including at least a first tier, a second tier, and a third tier, the tiers defining progressively granular identifiers for associating instructional content and performance data, wherein executing the remediation plan further comprises applying a tier-specific rule object comprising a time window and a score threshold to performance data tagged to the plurality of hierarchical tiers, and conditionally including one or more steps responsive to whether the score threshold is satisfied within the time window.
14 . The system of claim 9 , wherein execution of the remediation plan includes generating, for a question step, a compact evaluation prompt comprising an approved reference answer associated with an approved Q&A record, wherein the method further includes limiting permitted attempts and, in response to an incorrect attempt, appending progressively specific hint directives such that feedback is generated from the approved reference answer and an optional approved summary without disclosing the approved reference answer.
15 . The system of claim 9 , wherein constructing the constrained prompts further comprises caching static summary portions and reusing a cache identifier in subsequent prompts to reduce token transmission; and determining a token count for an assembled prompt prior to inference and, in response to the token count exceeding a budget threshold, selecting a reduced set of approved summaries and the candidate Q&A records for inclusion in the assembled prompt.
16 . The system of claim 9 , wherein the summaries are stored in dual formats including display-optimized markup and token-optimized text.