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🤹‍♂️ seppl

AI-Powered E-Learning App for Argument Analysis.

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Gallery • Highlights • Background • Technical Notes

Warning

🤹‍♂️ seppl has been discontinued by DebateLab@KIT in Feb 2023. We're providing the code in this archived repository as is and without warranty. If you have any questions or would like to pick up the project, please contact us.

Gallery

Screenshot01

Screenshot01

Screenshot01

Highlights

⭐️ Online e-Tutor that walks students through complex argumentation analysis problems.

⭐️ Structures the reconstruction workflow into high-level stages and individual steps.

⭐️ Provides instant feedback on the correctness of each individual reconstruction step and suggests improvements.

⭐️ Works with arbitrary argumentative source texts.

⭐️ User management for weekly assignments and global learning progress.

Background

➡️ Conceives of argument reconstruction as a hermeneutical process, in which analysts constantly revise their logical reconstruction in line with different criteria:

  • Plausibility and faithfulness of premises and conclusion.
  • Inferential strength (e.g., deductive correctness) of the reconstructed argument.
  • Dialectic function of the reconstructed argument in its given context.

➡️ Structures the reconstruction workflow into the following stages:

  1. Informal analysis: note gist, title, main conclusion
  2. Premise-conclusion structure: state premises, conclusion, intermediate conclusions
  3. Faithfulness: identify quotes from source text that correspond to premises and conclusion
  4. Inference graph: informally lay out the inferential relations between argument's propositions
  5. Deductive correctness: formalize premises and conclusion, check deductive validity

➡️ Current stage of the reconstruction process is determined by automatic evaluation of reconstruction given latest revision, so users may "fall back" and progress is non-cumulative.

➡️ Users may resume reconstruction from any previous state, allowing for branching and non-linear hermeneutical processes.

➡️ Based on the DeepA2 framework and custom T5 models fine-tuned on critical thinking datasets.

➡️ Software architecture: Uses decomposed prompting with various generative and loss LLM calls, organized as chain of responsibility.

➡️ Built in the summer 2022.

➡️ Deployed in winter term 2022/23 in critical thinking course at the Karlsruhe Institute of Technology.

➡️ Evaluated with a Teaching Analysis Poll (TAP) by the Methods Lab at the House of Competence (www.hoc.kit.edu) in collaboration with KIT's quality management services (https://www.sts.kit.edu/qualitaetsmanagement.php). 🙏

➡️ Further reading:

Technical Notes

Docker

# build image (optional)
docker build -t seppl:latest .

# run app
docker run -ti --rm --expose 8080 --network host seppl:latest

Access docker via bash:

docker run -ti --rm seppl:latest bash

Prover9

There are issues with installing prover9.

In compiling with gcc 7.4.0, we get error:

gcc -O -Wall -lm -o prover9 prover9.o index_lits.o forward_subsume.o demodulate.o pred_elim.o unfold.o semantics.o giv_select.o white_black.o actions.o search.o utilities.o provers.o foffer.o ../ladr/libladr.a search.o: In function search': search.c:(.text+0x6654): undefined reference to round'

This is because the make directive is not of the correct (or new) format:

prover9: prover9.o $(OBJECTS) $(CC) $(CFLAGS) -lm -o prover9 prover9.o $(OBJECTS) ../ladr/libladr.a

It should be:

prover9: prover9.o $(OBJECTS) $(CC) $(CFLAGS) -o prover9 prover9.o $(OBJECTS) ../ladr/libladr.a -lm

There are also unused variables such as: mindex.c:650:11: warning: variable ‘tr’ set but not used [-Wunused-but-set-variable] Trail tr;

These defects can be fixed easily.